Authorship：Last author   Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：IEEE  

DOI： 10.1109/iv60283.2023.00030

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Authorship：Last author,　Corresponding author   Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：Research Square Platform LLC  

Abstract

This paper proposes a cost-effectiveness metric for association rule mining suitable for software defect prediction where conditions of defective modules are characterized as association rules.Given a certain amount of test effort (or the number of test cases), the proposed metric is the expected number of defects to be discovered in modules that meet an association rule.Since the amount test effort is limited in general and full testing of all modules is ineffective, the proposed metric is useful to focus on the most cost effective set of modules to be tested with limited test effort. The proposed metric is defined based on the exponential Software Reliability Growth Model (SRGM) extended with the module size parameter, assuming that a larger module require more effort to discover defects. To evaluate the effectiveness of the proposed metric, association rules were extracted and prioritized based on the proposed metric using data sets of four open source software projects. The LOC-based cumulative-lift chart, which is often used to evaluate the cost effectiveness of defect prediction, shows that the proposed metric can focus on the rules that can discover more defects than the conventional association rule metrics, confidence and odds ratio.

DOI： 10.21203/rs.3.rs-1988568/v1

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Other Link： https://www.researchsquare.com/article/rs-1988568/v1.html

Authorship：Last author   Language：English   Publishing type：Research paper (scientific journal)   Publisher：Institute of Electrical and Electronics Engineers (IEEE)  

DOI： 10.1109/access.2023.3266731

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Authorship：Last author,　Corresponding author   Language：English   Publishing type：Research paper (international conference proceedings)  

DOI： 10.1109/SERA57763.2023.10197645

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Other Link： https://dblp.uni-trier.de/db/conf/sera/sera2023.html#MatsumotoNSM23

Publishing type：Research paper (international conference proceedings)  

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Publishing type：Research paper (international conference proceedings)   Publisher：IEEE  

DOI： 10.1109/iiai-aai-winter58034.2022.00021

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Authorship：Last author   Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：IEEE  

DOI： 10.1109/iiai-aai-winter58034.2022.00041

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Publishing type：Research paper (international conference proceedings)   Publisher：IEEE  

DOI： 10.1109/iiai-aai-winter58034.2022.00030

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Authorship：Last author,　Corresponding author  

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Publishing type：Research paper (scientific journal)   Publisher：Institute of Electrical and Electronics Engineers (IEEE)  

DOI： 10.1109/access.2022.3188246

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Publishing type：Research paper (international conference proceedings)  

DOI： 10.1145/3524842.3529093

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Other Link： https://dblp.uni-trier.de/db/conf/msr/msr2022.html#TsunodaMTTBNNM22

Publishing type：Research paper (international conference proceedings)  

Most e-learning systems deliver solely visual information, even though they boast a huge potential for supporting the learners using various other capabilities (e.g. camera, speakers) of the hosting platform (i.e. computer, smart phone etc.). In this study, we focus deploying one such potential, namely audio stimuli (informative and non-informative), for supporting rote learning of different types of learning material (i.e. easy verbal, hard verbal and numerical). Our results indicate that audio stimuli do not provide a significant benefit for studying easy verbal content, but there is a big room for improvement concerning other content types (hard verbal and numerical). Interestingly, despite the general implications of dual-coding theory, human-readout of hard verbal contents is observed not to provide any significant improvement over visual-only stimuli. However, to our surprise, non-informative audio stimuli (i.e. bell sound) are observed to provide an improvement, whereas numerical content is observed to benefit in a similar way from informative and non-informative audio. Based on these results, in the future we aim developing an automatic learning support system, which triggers the appropriate audio stimuli, taking in consideration the type of content.

DOI： 10.1109/IIAIAAI55812.2022.00057

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Publishing type：Research paper (international conference proceedings)  

This study focuses on a specific type of subset selection problem, which is constrained in terms of the rank bi-serial correlation (RBSC) coefficient of the outputs. For solving such problems, we propose an approach with several advantages such as (i) providing a clear insight into the feasibility of the problem with respect to the hyper-parameters, (ii) being non-iterative, (iii) having a foreseeable running time, and (iv) with the potential to yield non-deterministic (diverse) outputs. In particular, the proposed approach is based on starting from a composition of subsets with an extreme value of the RBSC coefficient (e.g. ρ=1) and swapping certain elements of the subsets in order to adjust ρ into the desired range. The proposed method is superior to the previously proposed RBSC-SubGen, which attempts to solve the problem before confirming its feasibility, taking random steps, and has unforeseeable running times and saturation issues.

DOI： 10.1109/IIAIAAI55812.2022.00076

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Publishing type：Research paper (scientific journal)  

In evaluating the performance of software defect prediction models, accuracy measures such as precision and recall are commonly used. However, most of these measures are affected by neg/pos ratio of the data set being predicted, where neg is the number of negative cases (defect-free modules) and pos is the number of positive cases (defective modules). Thus, it is not fair to compare such values across different data sets with different neg/pos ratios and it may even lead to misleading or contradicting conclusions. The objective of this study is to address the class imbalance issue in assessing performance of defect prediction models. The proposed method relies on computation of expected values of accuracy measures based solely on the value of the neg and pos values of the data set. Based on the expected values, we derive the neg/pos-normalized accuracy measures, which are defined as their divergence from the expected value divided by the standard deviation of all possible prediction outcomes. The proposed measures enable us to provide a ranking of predictions across different data sets, which can distinguish between successful predictions and unsuccessful predictions. Our results derived from a case study of defect prediction based on 19 defect data sets indicate that ranking of predictions is significantly different than the ranking of conventional accuracy measures such as precision and recall as well as composite measures F1-value, AUC of ROC, MCC, G-mean and Balance. In addition, we conclude that MCC attains a better defect prediction accuracy than F1-value, AUC of ROC, G-mean and Balance.

DOI： 10.1109/ACCESS.2022.3232144

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Publishing type：Research paper (international conference proceedings)   Publisher：IEEE  

DOI： 10.1109/IV56949.2022.00034

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Other Link： https://dblp.uni-trier.de/db/conf/iv/iv2022.html#SasakuraTM22

Language：English   Publishing type：Research paper (international conference proceedings)  

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Language：English   Publishing type：Research paper (international conference proceedings)  

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Authorship：Corresponding author   Publishing type：Research paper (scientific journal)  

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Authorship：Corresponding author   Language：English   Publishing type：Research paper (international conference proceedings)  

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Authorship：Corresponding author   Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：IEEE  

DOI： 10.1109/SNPD51163.2021.9704932

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Other Link： https://dblp.uni-trier.de/db/conf/snpd/snpd2021.html#NakamuraMSU21

Authorship：Corresponding author   Language：English   Publishing type：Research paper (international conference proceedings)  

The correlation coefficient is commonly used in analyses of software project data sets for the purpose of quantifying the relationship between two variables. However, while there are various types of relationships between two variables, the correlation coefficient cannot distinguish between these types. This study proposes new metrics between two continuous variables that have the potential to characterize the relationship types.

DOI： 10.1109/SNPD51163.2021.9704983

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Authorship：Corresponding author   Language：English   Publishing type：Research paper (international conference proceedings)  

Software quality assurance (SQA) is a series of activities within the software development lifecycle that repetitively verify or test the software deliverables to ensure their quality. In this paper, we propose a simulation model of SQA to quantitatively demonstrate the positive effect of adding quality assurance (QA) effort especially in early phases of software development. The proposed model can represent the relationship among the number of bugs in each phase, the amount of QA effort, the expected number of detectable bugs and the amount of bug fixing effort. The model can simulate the different QA strategies in a given software development context; thus, it is useful to identify the best or better strategies to improve software quality with smaller QA and bug fixing effort.

DOI： 10.1109/SNPD51163.2021.9704927

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Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：IEEE  

DOI： 10.1109/icsme52107.2021.00074

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Authorship：Corresponding author   Publishing type：Research paper (scientific journal)  

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：Institute of Electronics, Information and Communications Engineers (IEICE)  

DOI： 10.1587/transinf.2020edl8098

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：Institute of Electronics, Information and Communications Engineers (IEICE)  

DOI： 10.1587/transinf.2020mpp0004

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Language：English   Publishing type：Research paper (scientific journal)  

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Publishing type：Research paper (scientific journal)  

In this study, we quantitatively compare the effects of outlier handling methods in training datasets for model building on eight software effort estimation models (e.g., linear multiple regression, regression trees, random forests, support vector regression, etc.), and we evaluate the effectiveness of the data smoothing method proposed by the authors. In our experiments, we compare three outlier removal methods (outlier removal using Cook's distance, TEAK, and Filter-INC) in addition to the data smoothing method. Experimental results showed that the data smoothing method combined with the outlier detection method in Cook's distance or Filter-INC were found to build a model with good estimation performance.

DOI： 10.11309/jssst.38.3_83

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Publishing type：Research paper (scientific journal)  

Recently, automated machine learning (AutoML), which automates pre-processing, model selection, and hyperparameter adjustment, is becoming more and more popular, and it is expected to provide both ease of model construction and high prediction accuracy. In this study, we apply AutoML to software development effort estimation and experimentally evaluate its effectiveness. In our experiments, we employed auto-sklearn, which is an AutoML library, as well as linear multiple regression, elastic net, and random forest for comparison. A comparison of the estimation accuracy of each model by the win-tie-loss method confirmed that auto-sklearn showed the same or better estimation performance than other models. We also summarize the results of analyzing the effect of search time of auto-sklearn on the estimation accuracy.

DOI： 10.11309/jssst.38.4_46

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Authorship：Corresponding author   Publishing type：Research paper (scientific journal)  

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Authorship：Corresponding author   Publishing type：Research paper (conference, symposium, etc.)  

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Authorship：Corresponding author   Publishing type：Research paper (conference, symposium, etc.)  

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Authorship：Corresponding author   Publishing type：Research paper (conference, symposium, etc.)  

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Authorship：Corresponding author   Publishing type：Research paper (conference, symposium, etc.)  

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Authorship：Corresponding author   Publishing type：Research paper (conference, symposium, etc.)  

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Authorship：Corresponding author   Publishing type：Research paper (conference, symposium, etc.)  

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Authorship：Corresponding author   Publishing type：Research paper (conference, symposium, etc.)  

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Authorship：Corresponding author   Publishing type：Research paper (conference, symposium, etc.)  

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Authorship：Corresponding author   Language：English   Publishing type：Research paper (scientific journal)   Publisher：Institute of Electronics, Information and Communications Engineers (IEICE)  

DOI： 10.1587/transinf.2019edp7150

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Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：IEEE  

DOI： 10.1109/iiai-aai50415.2020.00036

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Authorship：Corresponding author   Publishing type：Research paper (conference, symposium, etc.)  

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Authorship：Corresponding author   Publishing type：Research paper (conference, symposium, etc.)  

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：Institute of Electronics, Information and Communications Engineers (IEICE)  

This study focuses on computer based foreign language vocabulary learning systems. Our objective is to automatically build vocabulary decks with desired levels of relative difficulty relations. To realize this goal, we exploit the fact that word frequency is a good indicator of vocabulary difficulty. Subsequently, for composing the decks, we pose two requirements as uniformity and diversity. Namely, the difficulty level of the cards in the same deck needs to be uniform enough so that they can be grouped together and difficulty levels of the cards in different decks need to be diverse enough so that they can be grouped in different decks. To assess uniformity and diversity, we use rank-biserial correlation and propose an iterative algorithm, which helps in attaining desired levels of uniformity and diversity based on word frequency in daily use of language. In experiments, we employed a spaced repetition flashcard software and presented users various decks built with the proposed algorithm, which contain cards from different content types. From users' activity logs, we derived several behavioral variables and examined the polyserial correlation between these variables and difficulty levels across different word classes. This analysis confirmed that the decks compiled with the proposed algorithm induce an effect on behavioral variables in line with the expectations. In addition, a series of experiments with decks involving varying content types confirmed that this relation is independent of word class.

DOI： 10.1587/transinf.2019edp7279

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：International Institute of Applied Informatics  

DOI： 10.52731/ijltle.v3.i1.521

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：TAYLOR & FRANCIS INC  

E-learning offers many advantages like being economical, flexible and customizable, but also has challenging aspects such as lack of - social-interaction, which results in contemplation and sense of remoteness. To overcome these and sustain learners' motivation, various stimuli can be incorporated. Nevertheless, such adjustments initially require an assessment of engagement level. In this respect, we propose estimating engagement level from facial landmarks exploiting the facts that (i) perceptual decoupling is promoted by blinking during mentally demanding tasks; (ii) eye strain increases blinking rate, which also scales with task disengagement; (iii) eye aspect ratio is in close connection with attentional state and (iv) users' head position is correlated with their level of involvement. Building empirical models of these actions, we devise a probabilistic estimation framework. Our results indicate that high and low levels of engagement are identified with considerable accuracy, whereas medium levels are inherently more challenging, which is also confirmed by inter-rater agreement of expert coders.

DOI： 10.1080/10447318.2020.1768666

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Authorship：Corresponding author   Publishing type：Research paper (conference, symposium, etc.)  

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Authorship：Corresponding author   Publishing type：Research paper (conference, symposium, etc.)  

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Authorship：Corresponding author   Publishing type：Research paper (conference, symposium, etc.)  

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Authorship：Corresponding author   Publishing type：Research paper (conference, symposium, etc.)  

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Authorship：Corresponding author   Publishing type：Research paper (conference, symposium, etc.)  

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Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：IEEE  

DOI： 10.1109/fie43999.2019.9028486

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Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：ACM  

This study explores estimation of grasping region of objects from gaze data. Our study distinguishes from previous works by accounting for "grasping uniformity" of the objects. In particular, we consider three types of graspable objects: (i) with a well-defined graspable part (e.g. handle), (ii) without a grip but with an intuitive grasping region, (iii) without any grip or intuitive grasping region. We assume that these types define how "uniform" grasping region is across different graspers. In experiments, we use "Learning to grasp" data set and apply the method of [5] for estimating grasping region from gaze data. We compute similarity of estimations and ground truth annotations for the three types of objects regarding subjects (a) who perform free viewing and (b) who view the images with the intention of grasping. In line with many previous studies, similarity is found to be higher for non-graspers. An interesting finding is that the difference in similarity (between free viewing and motivated to grasp) is higher for type-iii objects; and comparable for type-i and ii objects. Based on this, we believe that estimation of grasping region from gaze data offers a larger potential to "learn" particularly grasping of type-iii objects.

DOI： 10.1145/3349537.3352787

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Publishing type：Research paper (international conference proceedings)  

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Authorship：Corresponding author   Publishing type：Research paper (conference, symposium, etc.)  

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Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：Springer International Publishing  

DOI： 10.1007/978-3-030-26118-4_8

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Authorship：Corresponding author   Publishing type：Research paper (scientific journal)  

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Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：IEEE  

This study focuses on foreign language vocabulary learning in computerized medium and seeks for any possibility of adaptation with respect to background information on word classes. To that end, we employ a spaced repetition flashcard software and display English vocabulary belonging to three word classes as (i) abstract noun, (ii) concrete noun, and (iii) verb. Regarding each word class, we deploy three sets of words with difficulty levels of (i) easy, (ii) medium, and (iii) hard. Through log file analysis, we derive several behavioral variables and examine the polyserial correlation between these variables and difficulty levels across different word classes. It is found that abstract and concrete nouns do not have any significant difference in terms of the correlation for the five kinds of behavioral variables in focus. However, it is noted that front sides of the cards involving verbs are observed relatively longer, while back sides are observed for somewhat shorter duration.

DOI： 10.1109/iiai-aai.2019.00052

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Authorship：Corresponding author   Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：IEEE  

DOI： 10.1109/SNPD.2019.8935642

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Authorship：Corresponding author   Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：IEEE  

The goal of this paper is to improve the estimation performance of software development effort by mitigating the problem caused by outliers in a historical software project data set, which is used to construct an effort estimation model. To date, outlier removal methods have been proposed to solve this problem; however, they are not always effective because removing outliers reduces the number of data points (= software projects in our case) in a data set, and a model built from a small data set often suffers from lack of generality. In such a case, estimation performance can become even worse. In this paper we propose a method called data smoothing to mitigate the problem of outliers without reducing the number of data points. We consider that data points are outliers if they do not meet the assumption of Analogy-Based Estimation (ABE) such that "projects with similar features require similar development efforts." The proposed method changes the effort values (person-months or person-hours) in a data set so as to satisfy this assumption; and by this way, all outliers become non-outliers without decreasing the data points. As a result of experimental evaluation using 8 software development data sets, we found that the proposed data smoothing showed the same or higher effort estimation accuracy than the non-smoothing case, while conventional outlier removal method showed worse accuracy in some data set.

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Authorship：Corresponding author   Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：IEEE  

The effectiveness of defect prediction depends on modeling techniques as well as their parameter optimization, data preprocessing and ensemble development. This paper focuses on auto-sklearn, which is a recently-developed software library for automated machine learning, that can automatically select appropriate prediction models, hyperparameters and data preprocessing techniques for a given data set and develop their ensemble with optimized weights. In this paper we empirically evaluate the effectiveness of auto-sklearn in predicting the number of defects in software modules. In the experiment, we used software metrics of 20 OSS projects for cross-release defect prediction and compared auto-sklearn with random forest, decision tree and linear discriminant analysis by using Norm(Popt) as a performance measure. As a result, auto-sklearn showed similar prediction performance as random forest, which is one of the best prediction models for defect prediction in past studies. This indicates that auto-sklearn can obtain good prediction performance for defect prediction without any knowledge of machine learning techniques and models.

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Publishing type：Research paper (scientific journal)  

In this paper, we propose an association rule A ) Correl(X, Y ) that handles a correlation function, where A is the prerequisite and Correl(X, Y ) is the correlation between variables X and Y . With this extension, we can find conditions whose correlation of arbitrary two variables is high (or low) from a given data set. Furthermore, in order to distinguish statistically significant correlation, we define the rule A ) TestCorrel(X, Y ) which holds the result of the correlation significance test in the conclusion section, where TestCorrel(X, Y ) is a p-value of no-correlation test between X and Y . In order to confirm the feasibility of the proposed method, a case study using software development data was conducted. We found that it is possible to distinguish projects that are suitable for predicting development effort and those that are not.

DOI： 10.11309/jssst.36.3_47

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Publishing type：Research paper (scientific journal)  

Auto-sklearn is a recent attention-gathering software library for automated machine learning that can au-tomatically select appropriate prediction models and hyper parameters for a given data set. In this paper we empirically evaluate the effectiveness of auto-sklearn in software bug prediction. In the experiment, we used software metrics of 20 OSS projects for inter-version bug prediction and compared auto-sklearn with random forrest, decision tree and linear descriminat analysis by using AUC of ROC curve as a performance measure. As a result, auto-sklearn showed similar prediction performance as random forrest. We conclude that, although auto-sklearn is useful for bug prediction, we cannot expect better prediction performance than conventional modeling techniques.

DOI： 10.11309/jssst.36.4_46

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Authorship：Corresponding author   Publishing type：Research paper (scientific journal)   Publisher：Institute of Electronics, Information and Communications Engineers (IEICE)  

DOI： 10.1587/transinf.2018edp7020

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Language：Japanese   Publishing type：Research paper (scientific journal)   Publisher：Japan Society for Software Science and Technology  

In this paper, focusing on the problem that estimation accuracy of software development effort greatly varies among software projects, we propose a two-step effort estimation method, and conduct a feasibility study of the method. In the proposed method, given a project to be estimated, we evaluate the dependability of effort estimation for the project, that is, we evaluate whether the effort can be accurately estimated or not for the project. In case that the dependability is high, we conduct the estimation. Otherwise, estimation is avoided. As a result of an experiment to assess the feasibility of the proposed method, the residual square mean turned out to be a suitable measure of dependability
and, by avoiding the estimation when a project belongs to the high residual square mean group, the estimation error was reduced, which indicates the effectiveness of the two-step estimation. Also, by relaxing the conditions to avoid the estimation, we found that it is possible to increase the number of project to be estimated, while decreasing the estimation error.

DOI： 10.11309/jssst.35.1_194

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Language：Japanese   Publishing type：Research paper (scientific journal)   Publisher：日本ソフトウェア科学会  

<p>This paper presents a feasibility study to predict software bugs in statement level. The proposed method uses a window to inspect a given source file containing a bug. By moving the window from the top to the end of a source file, a set of buggy windows and non-buggy windows are derived. Then, we construct a spam filter-based bug prediction model, whose input is a set of tokens in a window and output is the probability of containing a bug in the window. Finally, we integrate bug prediction results on these windows to compute the probability of containing a bug in each statement. We conducted a feasibility study using 593 source files each containing one bug, and found that, by using the size=3 window, we could identify 67.1% of bugs in top 10 buggy statements of each source file prioritized by our bug prediction, which suggests that the bug pinpointing is feasible.</p>

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Publishing type：Research paper (international conference proceedings)   Publisher：IEEE  

DOI： 10.1109/APSEC.2018.00065

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Publishing type：Research paper (international conference proceedings)   Publisher：CEUR-WS.org  

—To conduct empirical research on industry software development, it is necessary to obtain data of real software projects from industry. However, only few such industry data sets are publicly available; and unfortunately, most of them are very old. In addition, most of today’s software companies cannot make their data open, because software development involves many stakeholders, and thus, its data confidentiality must be strongly preserved. This paper proposes a method to artificially generate a “mimic” software project data set whose characteristics (such as average, standard deviation and correlation coefficients) are very similar to a given confidential data set. The proposed method uses the Box–Muller method for generating normally distributed random numbers, then, exponential transformation and number reordering are used for data mimicry. Instead of using the original (confidential) data set, researchers are expected to use the mimic data set to produce similar results as the original data set. To evaluate the usefulness of the proposed method, effort estimation models were built from an industry data set and its mimic data set. We confirmed that two models are very similar to each other, which suggests the usefulness of our proposal.

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：Taru Publications  

To detect the software theft, software birthmarks have been proposed. Software birthmark systems extract software birthmarks, which are native characteristics of software, from binary programs, and compare them by computing the similarity between birthmarks. This paper proposes a new procedure for scaling up the birthmark systems. While conventional birthmark systems are composed of the birthmark extraction phase and the birthmark comparison phase, the proposed method adds two new phases between extraction and comparison, namely, compression phase, which employs fuzzy hashing, and pre-comparison phase, which aims to increase distinction property of birthmarks. The proposed method enables us to reduce the required time in the comparison phase, so that it can be applied to detect software theft among many larger scale software products. From an experimental evaluation, the authors found that the proposed method significantly reduces the comparison time, and keeps the distinction performance, which is one of the important properties of the birthmark. Also, the preservation performance is acceptable when the threshold value is properly set.

DOI： 10.4018/IJSI.2017070107

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：IEEE COMPUTER SOC  

Software engineering beliefs-short, attention-getting, practically useful statements-can help to justify process improvements. The authors empirically validate four selected beliefs in relation to the increase or decrease of defects in system testing.

DOI： 10.1109/MITP.2017.31

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：IEICE-INST ELECTRONICS INFORMATION COMMUNICATIONS ENG  

To identify problems in a software development process, we have been developing an automated measurement tool called TaskPit, which monitors software development tasks such as programming, testing and documentation based on the execution history of software applications. This paper introduces the system requirements, design and implementation of TaskPit; then, presents two real-world case studies applying TaskPit to actual software development. In the first case study, we applied TaskPit to 12 software developers in a certain software development division. As a result, several concerns (to be improved) have been revealed such as (a) a project leader spent too much time on development tasks while he was supposed to be a manager rather than a developer, (b) several developers rarely used e-mails despite the company's instruction to use e-mail as much as possible to leave communication records during development, and (c) several developers wrote too long e-mails to their customers. In the second case study, we have recorded the planned, actual, and self reported time of development tasks. As a result, we found that (d) there were unplanned tasks in more than half of days, and (e) the declared time became closer day by day to the actual time measured by TaskPit. These findings suggest that TaskPit is useful not only for a project manager who is responsible for process monitoring and improvement but also for a developer who wants to improve by him/herself.

DOI： 10.1587/transinf.2016EDP7222

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：ELSEVIER SCIENCE BV  

Recently, size of information system gets large, and the information system operation (IT operations) is often outsourced. When IT operations of the large system is outsourced, high cost is needed, and troubles in IT operations may affect the activity of the company. Therefore, the information system operation is important for the companies. Cost is one of the important factors when the system operation is outsourced. However, it is not easy for the customers (system users) to judge whether the operation cost is valid or not. To support the judgment, we focus on information which the customers can know (e.g., size of software), to estimate the working time. In the analysis, we clarified the factors which affect the working time. Then, data was stratified based on the factors, to show the benchmark of working time. Using the benchmark, customers estimate the working time roughly. Also, we clarified the factors which affect the unit cost, and showed the benchmark. Operation cost can be estimated, by estimated working time multiplied by the estimated unit cost. The analysis results showed that the process standardization relates to the working time of operators. Also, the network range and the contract type have a relationship to the unit cost of operators. Work efficiency and unit cost do not affect operation quality. (C) 2016 Elsevier B.V. All rights reserved.

DOI： 10.1016/j.scico.2016.10.006

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：SPRINGER  

Among numerous possible choices of effort estimation methods, analogy-based software effort estimation based on Case-based reasoning is one of the most adopted methods in both the industry and research communities. Solution adaptation is the final step of analogy-based estimation, employed to aggregate and adapt to solutions derived during the case-based reasoning process. Variants of solution adaptation techniques have been proposed in previous studies; however, the ranking of these techniques is not conclusive and shows conflicting results, since different studies rank these techniques in different ways. This paper aims to find a stable ranking of solution adaptation techniques for analogy-based estimation. Compared with the existing studies, we evaluate 8 commonly adopted solution techniques with more datasets (12), more feature selection techniques included (4), and more stable error measures (5) to a robust statistical test method based on the Brunner test. This comprehensive experimental procedure allows us to discover a stable ranking of the techniques applied, and to observe similar behaviors from techniques with similar adaptation mechanisms. In general, the linear adaptation techniques based on the functions of size and productivity (e.g., regression towards the mean technique) outperform the other techniques in a more robust experimental setting adopted in this study. Our empirical results show that project features with strong correlation to effort, such as software size or productivity, should be utilized in the solution adaptation step to achieve desirable performance. Designing a solution adaptation strategy in analogy-based software effort estimation requires careful consideration of those influential features to ensure its prediction is of relevant and accurate.

DOI： 10.1007/s10664-016-9434-8

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© 2016 IEEE. The purpose of this research is to compare textual programming languages and visual programming languages from the aspect of motivation. As a textual programming language, Processing programming language was used, and as visual programming languages, Scratch, a derivation of Scratch, Teaching materials offered by code.org, and LEGO Mindstorms EV3 were used. Teaching materials using the textual programming language, and those using the visual programming languages were developed separately. A trial experiment of programming education with the textual programming language was conducted to a cohort of seven primary schoolchildren. Trial experiments with the visual programming languages were conducted twice. In each of them, a cohort of eight primary schoolchildren participated. The motivation of the children was assessed using the questionnaire based on the ARCS (Attention, Relevance, Confidence, and Satisfaction) motivation model. The results with the visual programming languages suggested that the motivation scores of the children increased as the class progressed when visual programming languages were used. On the other hand, the results with Processing suggested that the variance of Satisfaction factor increased as the class progressed when textual programming languages were used, which further suggested that the Satisfaction scores of the children spread as the class progressed when textual programming languages were used.

DOI： 10.1109/FIE.2016.7757571

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© Copyright 2016 The Institute of Electronics, Information and Communication Engineers. GitHub is a developers' social networking service that hosts a great number of open source software (OSS) projects. Although some of the hosted projects are growing and have many developers, most projects are organized by a few developers and face difficulties in terms of sustainability. OSS projects depend mainly on volunteer developers, and attracting and retaining these volunteers are major concerns of the project stakeholders. To investigate the population structures of OSS development communities in detail and conduct software analytics to obtain actionable information, we apply a demographic approach. Demography is the scientific study of population and seeks to identify the levels and trends in the size and components of a population. This paper presents a case study, investigating the characteristics of the population structures of OSS projects on GitHub, and shows population projections generated with the well-known cohort component method. We found that there are four types of population structures in OSS development communities in terms of experiences and contributions. In addition, we projected the future population accurately using a cohort component population projection method. This method predicts a population of the next period using a survival rate calculated from past population. To the best of our knowledge, this is the first study that applied demography to the field of OSS research. Our approach addressing OSS-related problems based on demography will bring new insights, since studying population is novel in OSS research. Understanding current and future structures of OSS projects can help practitioners to monitor a project, gain awareness of what is happening, manage risks, and evaluate past decisions.

DOI： 10.1587/transinf.2015EDP7363

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Language：Japanese   Publisher：Japan Society for Software Science and Technology  

DOI： 10.11309/jssst.33.1_27

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Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：Institute of Electrical and Electronics Engineers Inc.  

In the past, the authors had been analyzing motivation of the learners in programming education using the ARCS assessment metric. This metric had been used in the application experiment in 13 programming courses, and about 1,700 sets of data was collected. From these data, the learners' model, characteristics of the change of motivation, and ways of improving teaching materials had been clarified. However, these study results were obtained after the terms, when the programming courses were over, and thus did not contribute much to the ongoing programming education. For this reason, in this research, the methods for predicting the change of learners' motivation were studied so that the learners who may need support could be identified. The idea came from the experiment the authors conducted, in which the motivation of learners was analyzed by plotting the motivation scores of each factor in the ARCS model as a 3D graph. As a result, a decreasing tendency of motivation was observed when the distribution of the plot widened. After studying the tendency in detail, it was thought to be due to the influence of the variance of sub-level category scores. In the proposed method, the motivation of each learner is assessed in each lesson using the ARCS assessment metric. If variance of the motivation scores of a learner in a lesson is above a certain threshold value AND if mean of the scores has not decreased from the previous lesson, then the learner is identified as a candidate of learner who needs support at that lesson. In the application experiment, a programming course with 9 lessons was offered and 9 learners attended all the 9 lessons. In the experiment, 7 cases had been identified as the candidates of learners who need support, and out of those 7 cases, a decrease of motivation to less than average was observed in 5 cases.

DOI： 10.1109/FIE.2014.7044221

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In this paper, a method for improving the teaching materials of programming education is introduced, and the evaluation of the effects of using the strategy is presented. By using this method, the teachers of programming education will be able to assess and improve their teaching materials irrespective of their knowledge and experience of their teaching materials already used. In this method, the teaching materials were improved based on the statistical analysis of the motivation of students. Specifically, the motivation of students was measured for each lower category of ARCS motivation model with the authors' original questionnaire. The lower category in a particular lesson that showed a statistically significant decrease from the previous lesson was identified, and the improvement strategies for the lower category were selected from the list of motivation strategies in the ARCS model. The teaching materials of programming education were then improved based on the strategy. In this research, five lower categories of particular lessons in a programming course were identified, and the teaching materials were improved. The improved teaching materials were used in the following programming course, and the effects of the improvements were seen in three lower categories out of the identified five lower categories. © 2013 IEEE.

DOI： 10.1109/FIE.2013.6684920

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Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：Springer Verlag  

To prevent cost overrun of software projects, it is effective to predict the project which has high risk of cost overrun in the early phase of the project. In this paper, we clarify the risk factors which affect cost overrun. The risk factors are denoted by the questions such as Are the customer's project goals clear? The risk factors can be used as independent variables of the cost overrun prediction model. In the analysis, we used 290 projects' data collected in a software development company. The dataset was stratified by the project start time and the project size to eliminate their influence, and relationships between risk factors and cost overrun were analyzed with the correlation ratio. In addition, we focused risk factors which have strong and stable relationships to cost overrun, and analyzed them using the Sharpe ratio based index. As a result, we identified some risk factors which have relatively strong and stable relationships to cost overrun. After the analysis, we experimentally predicted cost overrun projects by collaborative filtering, using the risk factors as independent variables. The result suggested that cost overrun projects can be predicted by the risk factors. © 2013 Springer-Verlag Berlin Heidelberg.

DOI： 10.1007/978-3-642-32172-6_14

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Background: Accurate effort estimation is the basis of the software development project management. The linear regression model is one of the widely-used methods for the purpose. A dataset used to build a model often includes categorical variables denoting such as programming languages. Categorical variables are usually handled with two methods: the stratification and dummy variables. Those methods have a positive effect on accuracy but have shortcomings. The other handing method, the interaction and the hierarchical linear model (HLM), might be able to compensate for them. However, the two methods have not been examined in the research area. Aim: giving useful suggestions for handling categorical variables with the stratification, transforming dummy variables, the interaction, or HLM, when building an estimation model. Method: We built estimation models with the four handling methods on ISBSG, NASA, and Desharnais datasets, and compared accuracy of the methods with each other. Results: The most effective method was different for datasets, and the difference was statistically significant on both mean balanced relative error (MBRE) and mean magnitude of relative error (MMRE). The interaction and HLM were effective in a certain case. Conclusions: The stratification and transforming dummy variables should be tried at least, for obtaining an accurate model. In addition, we suggest that the application of the interaction and HLM should be considered when building the estimation model. Copyright 2012 ACM.

DOI： 10.1145/2372251.2372267

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This study describes a new method of development visualization along with empirical evidence of its usefulness.
Typically, development activities such as program design, programming, and unit testing are not disclosed to the procurement organization (project owner). However, during integration and testing, various issues require collaboration between the procurement organization and developers. When this occurs, it is important to make the development process visible. Recent reports indicate the usefulness for project management of various in-process project measurements which allow visualization of the formerly invisible software project progress [1-6].
Based on this background, the authors investigated a case study where in-process measurement during the integration and test phase helped to make development issues visible. In this study, data obtained from the integration and testing phase were compared to a development process model.
This model was based on the author's experience, and provided a vivid picture of the development activity. By applying in-process measurements in collaboration during the integration test phase, the development activity was clearly visualized, and the procurement organization understood problems.

DOI： 10.1142/S0218194011005323

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：IEEE COMPUTER SOC  

DOI： 10.1109/MS.2010.159

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Other Link： http://dblp.uni-trier.de/db/journals/software/software28.html#journals/software/MondenOMM11

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DOI： 10.11309/jssst.28.1_300

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Recently, software functional size becomes larger, and consequently, not only a software developer but also a software purchaser suffers considerable losses by software project failure. So avoiding project failure is also important for purchasers. Project supervision (monitoring and control) is expected for the purchaser to suppress risk of project failure. It is performed by sharing software metrics during the project for the purchaser to grasp the status of the project, and corrective actions are done based on analysis results of the metrics. Although there are some software measurement models, the models are not enough to describe how to confirm effects of project supervision. To acquire the effects certainly, the purchaser and the developer should quantitatively confirm whether the effects are acquired or not by project supervision. In addition, the models cannot represent corrective actions when symptoms of project failure are found. We propose the model for project supervision. The model explains planning, collecting data, transforming data, analyzing data, reaction toward found issues, and confirming effect of project supervision. With our model, project supervision can be described more rigorously. © 2011 IEEE.

DOI： 10.1109/IWSM-MENSURA.2011.31

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To prevent cost overrun of software projects, it is necessary for project managers to identify projects which have high risk of cost overrun in the early phase. So far, discriminant methods such as linear discriminant analysis and logistic regression have been used to predict cost overrun projects. However, accuracy of discriminant methods often becomes low when a dataset used for predict is imbalanced, i.e. there exists a large difference between the number of cost overrun projects and non cost overrun projects. In this paper, we compared accuracy of linear discriminant analysis, logistic regression, classification tree, Mahalanobis-Taguchi method, and collaborative filtering, by changing the percentage of cost overrun projects in the dataset. The result showed that collaborative filtering was highest accuracy among five methods. When the number of cost overrun projects and non cost overrun was balanced in the dataset, linear discriminant analysis was second highest accuracy, and when it was not balanced, Mahalanobis-Taguchi method was second highest among five methods. © 2011 IEEE.

DOI： 10.1109/ICIS.2011.71

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DOI： 10.1109/IWSM-MENSURA.2011.12

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DOI： 10.1109/IWSM-MENSURA.2011.9

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Predictive models can be used in the detection of fault prone modules using source code metrics as inputs for the classifier. However, there exist numerous structural measures that capture different aspects of size, coupling and complexity. Identifying a metric subset that enhances the performance for the predictive objective would not only improve the model but also provide insights into the structural properties that lead to problematic modules. Another difficulty in building predictive models comes from unbalanced datasets, which are common in empirical software engineering as a majority of the modules are not likely to be faulty. Oversampling attempts to overcome this deficiency by generating new training instances from the faulty modules. We present the results of applying searchbased metric selection and oversampling to three NASA datasets. For these datasets, oversampling results in the largest improvement. Metric subset selection was able to reduce up to 52% of the metrics without decreasing the predictive performance gained with oversampling. © 2010 IEEE.

DOI： 10.1109/CCECE.2010.5575249

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DOI： 10.1109/ICSM.2010.5609530

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Language：Japanese   Publishing type：Research paper (conference, symposium, etc.)   Publisher：電子情報通信学会  

It is important to detect low quality modules which are likely to have many defects at an early development phase for creating high quality software. Hence, various prediction models are proposed such as linear discriminant analysis, logistic regression analysis, and neural networks. These methods, however, use metrics measured mainly from a source code, thus they can only be applied after implementation is completed. We attempt to predict low quality modules using design documents metrics included in software tags. Using design documents metrics, we can predict when design is completed, thus our method contribute to reliability enhancement activities at an implementation phase. We applied multiple regression analysis and genetic programming, and detected low quality modules with high accuracy compared to a random selection method.

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DOI： 10.1007/s10799-009-0050-9

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Language：Japanese   Publishing type：Research paper (scientific journal)   Publisher：日本ソフトウェア科学会  

DOI： 10.11309/jssst.26.3_44

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Language：Japanese   Publishing type：Research paper (scientific journal)   Publisher：一般社団法人情報処理学会  

Various fault-prone detection models have been proposed to improve software reliability. However, while improvement of prediction accuracy was discussed, there was few discussion about how the models shuld be used in the field, i.e. how test effort should be allocated. Thus, improvement of software reliability by fault-prone module detection was not clear. In this paper, we proposed TEAR (Test Effort Allocation and software Reliability) model that represents the relationship among fault-prone detection, test effort allocation and software reliability. The result of simulations based on TEAR...

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To clarify the effect of collaborative filtering (CF) on recommending highgenerality / low-generality software components, we experimentally verified two hypotheses; (1) the recommendation accuracy of CF for high-generality components is better than that of conventional methods (random algorithm and user average algorithm) and (2) the recommendation accuracy of CF for lowgenerality components is better than that of the conventional methods. We evaluated recommendation accuracy of CF with a dataset containing 29 open source software development projects (including 2,558 used components). As ...

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Language：Japanese   Publisher：INFORMATION-TECHNOLOGY PROMOTION AGENCY,JAPAN  

DOI： 10.11186/secjournal.5.58

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ソフトウェア開発における多変数の実績データを用いた工数見積りでは，その精度を高めるうえで，見積りに不要な変数を除去することが必須となる．しかし，近年注目されているプロジェクト間の類似性に基づく工数見積り方法（analogy-based法）においては，その特性を活かした変数選択法が提案されていない．本論文では，「説明変数（プロジェクト特性）の値が似ているプロジェクトどうしは工数も似た値をとる」というanalogy-based法の考え方を踏襲し，目的変数である工数との相関係数が低い説明変数を除去することで，説明変数の値が似ていれば目的変数の値も似ているという状態を保つように変数選択する方法Correlation Threshold based Selection（CTS）を提案する．ソフトウェア開発企業で収集された3種類の開発実績データセットを用いた評価実験の結果，CTSは，従来手法であるステップワイズ変数選択法と比べて計算量が小さく，特に，カテゴリ変数の数が少ない場合に，安定して精度を改善することができた．Analogy-based estimation methods have been studied for deriving an accurate effort estimate required to complete a project; however, few studies have focused on their variable selection methods. Here, variables to be selected are predictor variables (e.g. development size and development duration, etc.) related to an objective variable (effort). In this paper, we propose Correlation Threshold based Selection (CTS) method that exploits the basic concept of analogy-based estimation where efforts are similar if predictor variables are similar. The proposed method requires few amount of calculation while it can accurately exclude variables that do not correlate with the effort. This paper experimentally evaluated the effectiveness of the proposed method by using three data sets collected from software development companies. The result showed that the proposed method provided high estimation accuracy for projects having larger number of predictor variables.

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Language：Japanese   Publishing type：Research paper (scientific journal)   Publisher：一般社団法人情報処理学会  

Analogy-based estimation methods have been studied for deriving an accurate effort estimate required to complete a project; however, few studies have focused on their variable selection methods. Here, variables to be selected are predictor variables (e.g. development size and development duration, etc.) related to an objective variable (effort). In this paper, we propose Correlation Threshold based Selection (CTS) method that exploits the basic concept of analogy-based estimation where efforts are similar if predictor variables are similar. The proposed method requires few amount of calcula...

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DOI： 10.1145/1414004.1414084

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Other Link： http://dblp.uni-trier.de/db/conf/esem/esem2008.html#conf/esem/TodaMM08

Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：ASSOC COMPUTING MACHINERY  

This paper proposes a fault-prone module prediction method that combines association rule mining with logistic regression analysis. In the proposed method, we focus on three key measures of interestingness of an association rule (support, confidence and lift) to select useful rules for the prediction. If a module satisfies the premise (i.e. the condition in the antecedent part) of one of the selected rules, the module is classified by the rule as either fault-prone or not. Otherwise, the module is classified by the logistic model. We experimentally evaluated the prediction performance of the proposed method with different thresholds of each rule interestingness measure (support, confidence and lift) using a module set in the Eclipse project, and compared it with three well-known fault-proneness models (logistic regression model, linear discriminant model and classification tree). The result showed that the improvement of the F1-value of the proposed method was 0.163 at maximum compared to conventional models.

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Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：ASSOC COMPUTING MACHINERY  

This paper proposes a novel method to generate synthetic project cases and add them to a fit dataset for the purpose of improving the performance of analogy-based software effort estimation. The proposed method extends conventional over-sampling method, which is a preprocessing procedure for n-group classification problems, which makes it suitable for any imbalanced dataset to be used in analogy-based system. We experimentally evaluated the effect of the over-sampling method to improve the performance of the analogy-based software effort estimation by using the Desharnais dataset. Results show significant improvement to the estimation accuracy by using our approach.

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DOI： 10.1145/1414004.1414079

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DOI： 10.1109/ICSEA.2008.49

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：International Association for Computer Information Systems  

This paper describes an empirical study to reveal factors influencing defect correction effort in software development. In the study we collected various attributes (metrics) of defects found in a typical medium-scale, multi-vendor information system development project in Japan over a six-month period. We then statistically analyzed the relationship between the defects' attributes and the correction effort. The analysis confirmed the well-known principle "defects are the more expensive the later they are detected" by revealing that defects detected in the "system test" were 4.88 times more expensive than those detected in the "coding/unit test". Another principle "defects are more expensive the longer they survive in software" was also confirmed by revealing that defects, which survived two or more development phases, were 4.44 times more expensive than those detected immediately. We also identified other factors, such as defect reproducibility, severity, and the cause of detection delay, that had a significant influence on the correction effort.

DOI： 10.1080/08874417.2008.11645308

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This paper proposes to use eye movements to characterize the performance of individuals in reviewing software documents. We design and implement a system called DRESREM, which measures and records eye movements of document reviewers. Based on the eye movements captured by eye tracking device, the system computes the line number of the document that the reviewer is currently looking at. The system can also record and play back how the eyes moved during the review process. To evaluate the effectiveness of the system we conducted an experiment to analyze 30 processes of source code review (6 programs, 5 subjects) using the system. As a result, we have identified a particular pattern, called scan, in the subject's eye movements. Quantitative analysis showed that reviewers who did not spend enough time on the scan took more time to find defects on average.

DOI： 10.1093/ietfec/e90-a.10.2290

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DOI： 10.1109/ESEM.2007.34

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To improve the prediction performance of fault-proneness models, this paper experimentally evaluates the effect of over and under sampling methods, which are preprocessing procedures for a fit dataset. The sampling methods are expected to improve the prediction performance when the fit dataset is imbalanced, i.e. there exists a large bias between the number of faultprone modules and the number of non-fault-prone modules. There is, however, no research that reports the effects of applying sampling methods to fault-proneness models. In the experiment, we evaluated the effects of four sampling...

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In this paper, we experimentally evaluate outlier detection methods, which detect data points that are far away from others in a data set, in terms of improving the prediction performance of fault-prone module detection models. In the experiment, we compared two outlier detection methods (MOA, LOFM) each applied to three well-known fault-prone module detection models (LDA, LRA, CT). The result showed that MOA improved Fl-values of all fault-proneness models (0.04 at minimum, 0.17 at maximum and 0.10 at mean) while improvements by LOFM were relatively small (-0.01 at minimum, 0.04 at maximum and 0.01 at mean).

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This paper describes an empirical study to reveal rules associated with defect correction effort. We defined defect correction effort as a quantitative (ratio scale) variable, and extended conventional (nominal scale based) association rule mining to directly handle such quantitative variables. An extended rule describes the statistical characteristic of a ratio or interval scale variable in the consequent part of the rule by its mean value and standard deviation so that conditions producing distinctive statistics can be discovered. As an analysis target, we collected various attributes of about 1,200 defects found in a typical medium-scale, multi-vendor (distance development) information system development project in Japan. Our findings based on extracted rules include: (1)Defects detected in coding/unit testing were easily corrected (less than 7% of mean effort) when they are related to data output or validation of input data. (2)Nevertheless, they sometimes required much more effort (lift of standard deviation was 5.845) in case of low reproducibility, (3)Defects introduced in coding/unit testing often required large correction effort (mean was 12.596 staff-hours and standard deviation was 25.716) when they were related to data handing. From these findings, we confirmed that we need to pay attention to types of defects having large mean effort as well as those having large standard deviation of effort since such defects sometimes cause excess effort. © 2007 IEEE.

DOI： 10.1109/MSR.2007.5

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This paper proposes a method to mine rules from a software project data set that contains a number of quantitative attributes such as staff months and SLOC. The proposed method extends conventional association analysis methods to treat quantitative variables in two ways: (1) the distribution of a given quantitative variable is described in the consequent part of a rule by its mean value and standard deviation so that conditions producing the distinctive distributions can be discovered. To discover optimized conditions, (2) quantitative values appearing in the antecedent part of a rule are divided into contiguous fine-grained partitions in preprocessing, then rules are merged after mining so that adjacent partitions are combined. The paper also describes a case study using the proposed method on a software project data set collected by Nihon Unisys Ltd. In this case, the method mined rules that can be used for better planning and estimation of the integration and system testing phases, along with criteria or standards that help with planning of outsourcing resources.

DOI： 10.2197/ipsjdc.3.518

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Language：English   Publisher：Information and Media Technologies Editorial Board  

This paper proposes a method to mine rules from a software project data set that contains a number of quantitative attributes such as staff months and SLOC. The proposed method extends conventional association analysis methods to treat quantitative variables in two ways: (1) the distribution of a given quantitative variable is described in the consequent part of a rule by its mean value and standard deviation so that conditions producing the distinctive distributions can be discovered. To discover optimized conditions, (2) quantitative values appearing in the antecedent part of a rule are divided into contiguous fine-grained partitions in preprocessing, then rules are merged after mining so that adjacent partitions are combined. The paper also describes a case study using the proposed method on a software project data set collected by Nihon Unisys Ltd. In this case, the method mined rules that can be used for better planning and estimation of the integration and system testing phases, along with criteria or standards that help with planning of outsourcing resources.

DOI： 10.11185/imt.2.999

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DOI： 10.1002/ecjc.20141

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To clarify the relation between controllable attributes of a software development and its productivity, this paper experimentally analyzed a software project repository (SEC repository), consisting of 253 enterprise software development projects in Japanese companies, established by Software Engineering Center (SEC), Information-technology Promotion Agency, Japan. In the experiment, as controllable attributes, we focused on the outsourcing ratio of a software project, defined as an effort outsourced to subcontract companies divided by a whole development effort, and on the effort allocation balance among development phases. Our major findings include both larger outsourcing ratio and smaller upstream process effort leads to worse productivity. Copyright 2006 ACM.

DOI： 10.1145/1137983.1137988

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Language：English   Publisher：Information and Media Technologies Editorial Board  

This paper presents a method to evaluate the risk of information leakage in software processes for security-sensitive applications. A software process is modeled as a series of sub-processes, each of which produces new work products from input products. Since a process is conducted usually by multiple developers, knowledge of work products is shared among the developers. Through the collaboration, a developer may share with others the knowledge of products that are not related to the process. We capture the transfer of such irrelevant product knowledge as information leakage in a software process. In this paper, we first formulate the problem of information leakage by introducing a formal software process model. Then, we propose a method to derive the probability that each developer d knows each work product p at a given process of software development. The probability reflects the possibility that someone leaked the knowledge of p to d. We also conduct three case studies to show the applicability of leakage to practical settings. In the case studies, we evaluate how the risk of information leakage is influenced by the collaboration among developers, the optimal developer assignment and the structure of the software process. As a result, we show that the proposed method provides a simple yet powerful means to perform quantitative analysis on information leakage in a security-sensitive software process.

DOI： 10.11185/imt.1.121

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：The Institute of Electronics, Information and Communication Engineers  

To detect the theft of Java class files efficiently, we propose a concept of Java birthmarks, which are unique and native characteristics of every class file. For a pair of class files p and q, if q has the same birthmark as p's, q is suspected as a copy of p. Ideally, the birthmarks should satisfy the following properties : (a) preservation - the birthmarks should be preserved even if the original class file is tampered with, and (b) distinction - independent class files must be distinguished by completely different birthmarks. Taking (a) and (b) into account, we propose four types of birthmarks for Java class files. To show the effectiveness of the proposed birthmarks, we conduct three experiments. In the first experiment, we demonstrate that the proposed birthmarks are sufficiently robust against automatic program transformation (93.3876% of the birthmarks were preserved). The second experiment shows that the proposed birthmarks successfully distinguish non-copied files in a practical Java application (97.8005% of given class files were distinguished). In the third experiment, we exploit different Java compilers to confirm that the proposed Java birthmarks are core characteristics independent of compiler-specific issues.

DOI： 10.1093/ietisy/e88-d.9.2148

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The code clone (a duplicated code section in the source files of software) is one of the factors that complicate software maintenance. However, few empirical studies have analyzed the status quo of code clones. In this paper we conduct an extensive analysis of code clones using 125 packages of open source software written in C language, and suggest guidelines for the allowable production of code clones. Our results showed 11.3% as the average CRate (clone inclusion rate). For software packages that did not include automatically generated code, the CRate was 9.7%, the in-module CRate 8.2%, and the inter-module CRate 1.3%. These rates can be used as criteria for the allowable amount of code clone production. This paper also presents our findings on factors of code clone production, the influence of code clone production on maintainability, and removal methods.

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Other Link： https://dblp.uni-trier.de/db/journals/jcis/jcis45.html#UchidaMOKMK05

Language：English   Publishing type：Research paper (international conference proceedings)  

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Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：Institute of Electronics, Information and Communication, Engineers, IEICE  

Many computer systems are designed to make it easy for end-users to install and update software. An undesirable side effect, from the perspective of many software producers, is that hostile end-users may analyze or tamper with the software being installed or updated. This paper proposes a way to avoid the side effect without affecting the ease of installation and updating. We construct a computer system M with the following properties: 1) the end-user may install a program P in any conveniently accessible area of M
2) the program P contains encoded instructions whose semantics are obscure and difficult to understand
and 3) an internal interpreter W, embedded in a non-accessible area of M, interprets the obfuscated instructions without revealing their semantics. Our W is a finite state machine (FSM) which gives context-dependent semantics and operand syntax to the encoded instructions in P
thus, attempts to statically analyze the relation between instructions and their semantics will not succeed. We present a systematic method for constructing a P whose instruction stream is always interpreted correctly regardless of its input, even though changes in input will (in general) affect the execution sequence of instructions in P. Our framework is easily applied to conventional computer systems by adding a FSM unit to a virtual machine or a reconfigurable processor. Copyright © 2005 The Institute of Electronics, Information and Communication Engineers.

DOI： 10.1093/ietfec/E88-A.1.112

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Language：Japanese   Publishing type：Research paper (scientific journal)   Publisher：The Institute of Electronics, Information and Communication Engineers  

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Language：English   Publishing type：Research paper (international conference proceedings)  

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：SPRINGER-VERLAG BERLIN  

Effort estimation methods are one of the important tools for project managers in controlling human resources of ongoing or future software projects. The estimations require historical project data including process and product metrics that characterize past projects. Practically, in using the estimation methods, it is a problem that the historical project data frequently contain substantial missing values. In this paper, we propose an effort estimation method based on Collaborative Filtering for solving the problem. Collaborative Filtering has been developed in information retrieval researchers, as one of the estimation techniques using defective data, i.e. data having substantial missing values. The proposed method first evaluates similarity between a target (ongoing) project and each past project, using vector based similarity computation equation. Then it predicts the effort of the target project with the weighted sum of the efforts of past similar projects. We conducted an experimental case study to evaluate the estimation performance of the proposed method. The proposed method showed better performance than the conventional regression method when the data had substantial missing values.

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Language：English   Publishing type：Research paper (international conference proceedings)  

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Language：English   Publishing type：Research paper (international conference proceedings)  

DOI： 10.1109/METRIC.2003.1232480

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Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：IEEE COMPUTER SOC  

Since many of today's application software provide users with too many functions, the users sometimes cannot find the useful functions. This paper proposes a recommendation system based on a collaborative filtering approach to let users discover useful functions at low cost for the purpose of improving the user's productivity in using application software. The proposed system automatically collects histories of software function execution (usage histories) from many users through the Internet. Based on the collaborative filtering approach, collected histories are used to recommend the user a set of candidate functions that may be useful to the individual user. This paper illustrates conventional filtering algorithms and proposes a new algorithm suitable for recommendation of software functions. The result of an experiment with a prototype recommendation system showed that the average napin of our algorithm was smaller,than that of the conventional algorithms; and, it also showed that the standard deviation of ndpm of our algorithm was smaller than that of the conventional algorithms. Furthermore, while every conventional algorithm had a case whose recommendation was worse than the random algorithm, our algorithm did not.

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Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：IEEE COMPUTER SOC  

In the field of legacy software maintenance, there unexpectedly arises a large number of implicit coding rules, which we regard as a cancer in software evolution. Since such rules are usually undocumented and each of them is recognized only by, a few members in a maintenance team, a person who is not aware of a rule often violates it while doing various maintenance activities such as adding a new functionality, or repairing faults. The problem here is not only, such a violation introduces a new fault but also the same kind of fault will be generated again and again in the future by different maintainers.
This paper proposes a method for detecting code fragments that violate implicit coding rules. In the method, an expert maintainer, firstly, investigates the cause of each failure, described in the past failure reports, and identifies all the implicit coding rules that lie behind the faults. Then, the code patterns violating the rules (which we call "faulty code patterns") are described in a pattern description language. Finally, the potential faulty code fragments are automatically detected by a pattern matching technique.
The result of a case study with large legacy software showed that 32.7% of the failures, which have been reported during a maintenance process, were due to the violation of implicit coding rules. Moreover, 152 faults existed in 772 code fragments detected by, the prototype matching system, while 111 of them were not reported.

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Publisher：IEEE Computer Society  

DOI： 10.1109/ISESE.2002.1166933

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Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：IEEE COMPUTER SOC  

Existing researches suggest that the code clone (duplicated code) is one of the factors that degrades the design and the structure of software and lowers the software quality such as readability and changeability. However, the influence of code clones on software quality has not been quantitatively clarified yet.
In this paper, we have tried to quantitatively clarify the relation between code clones and the software reliability and maintainability of twenty years old software. As a result, we found that modules having code clones (clone-included modules) are more reliable than modules having no code clone (non-clone modules) on average. Nevertheless, the modules having very large code clones (more than 200 SLOC) are less reliable than non-clone modules. We also found that clone-included modules are less maintainable (having greater revision number on average) than non-clone modules; and, modules having larger code clone are less maintainable than modules having smaller code clone.

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Language：Japanese   Publisher：The Institute of Electronics, Information and Communication Engineers  

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Publisher：IEEE Computer Society  

DOI： 10.1109/CMPSAC.2000.884716

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Authorship：Last author,　Corresponding author   Language：Japanese   Publishing type：Research paper, summary (national, other academic conference)  

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Authorship：Last author,　Corresponding author   Language：Japanese   Publishing type：Research paper, summary (national, other academic conference)  

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Publishing type：Internal/External technical report, pre-print, etc.  

Academic publications have been evaluated with the impact on research<br />
communities based on the number of citations. On the other hand, the impact of<br />
academic publications on industry has been rarely studied. This paper<br />
investigates how academic publications contribute to software development by<br />
analyzing publication citations in source code comments in open source software<br />
repositories. We propose an automated approach of detecting academic<br />
publications based on Named Entity Recognition, and achieve 0.90 in $F_1$ as<br />
detection accuracy. We conduct a large-scale study of publication citations<br />
with 319,438,977 comments collected from active 25,925 repositories written in<br />
seven programming languages. Our findings indicate that academic publications<br />
can be knowledge sources of software development, and there can be potential<br />
issues of obsoleting knowledge.

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Language：English   Publisher：IEEE Computer Society  

Sampling methods are known to impact defect prediction performance. These sampling methods have configurable parameters that can significantly affect the prediction performance. It is however, impractical to assess the effect of all the possible different settings in the parameter space for all the several existing sampling methods. A constant and easy to tweak parameter present in all sampling methods is the distribution of the defective and non-defective modules in the dataset known as Pfp (% of fault-prone modules). In this paper, we investigate and assess the performance of defect prediction models where the Pfp parameter of sampling methods are tweaked. An empirical experiment and assessment of seven sampling methods on five prediction models over 20 releases of 10 static metric projects indicate that (1) Area Under the Receiver Operating Characteristics Curve (AUC) performance is not improved after tweaking the Pfp parameter, (2) pf (false alarms) performance degrades as the Pfp is increased. (3) a stable predictor is difficult to achieve across different Pfp rates. Hence, we conclude that the Pfp parameter setting can have a large impact on the performance (except AUC) of defect prediction models. We thus recommend researchers experiment with the Pfp parameter of the sampling method since the distribution of training datasets vary.

DOI： 10.1109/APSEC.2017.76

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Language：English   Publisher：IEEE  

In this paper, focusing on the problem that estimation accuracy of software development effort greatly varies among software projects, we propose a predictability classification method for software projects before conducting effort estimation. In the proposed method, given a project to be estimated, we first evaluate whether the effort can be accurately estimated or not by identifying the project as "predictable" or "unpredictable". In case of predictable projects, we conduct the effort estimation. Otherwise, estimation is avoided. As a result of an experiment to assess the effectiveness of the proposed method using six industry datasets, (i) the mean square residual and residual variance are shown to be suitable measures for recognition of predictability; and (ii) the average absolute error is significantly reduced in five datasets, by avoiding the estimation when a project belongs to the unpredictable class, which proves the effectiveness of the proposed method. By using the proposed method, practitioners become aware of cases when they can rely on the estimation and when they cannot.

DOI： 10.1109/BCD2018.2018.00015

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Language：English   Publisher：IEEE  

This paper proposes extended association rule mining that can deal with correlation functions. The extended association rule is expressed in the form of: A double right arrow Correl(X; Y) where Correl(X; Y) is a correlation function with two variables X and Y. By this extension, data analysts can discover the condition A that lead to low (or high) correlation between two given variables from a large dataset. In order to show the efficacy of the proposed method, a case study is performed on an industry dataset of software developments, assuming the scenario of discovering a condition, where software development effort is predictable (or unpredictable) from the size of the project, i.e. there exists a significantly high (or low) correlation between size and effort. Since such a condition cannot be obtained by conventional association rule mining, we confirm the efficiency of the proposed extended association rule mining.

DOI： 10.1109/BCD2018.2018.00020

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Language：English   Publisher：Springer Verlag  

Project management activities such as scheduling and project progress management are important to avoid project failure. As a basis of project management, effort estimation plays a fundamental role. To estimate software development effort by mathematical models, variables which are fixed before the estimation are used as independent variables. Some studies used team size and project duration as independent variables. Although they are sometimes fixed because of the limitation of human resources or business schedule, they may change by the end of the project. For instance, when delivery is delayed, actual duration and estimated duration is different. So, although using team size and project duration may enhance estimation accuracy, the error may also lower the accuracy. To help practitioners to select independent variables, we analyzed whether team size and duration should be used or not, when we consider the error included in the team size and the duration. In the experiment, we assumed that duration and team size include errors when effort is estimated. To analyze influence of the errors, we add n% errors to duration and team size. As a result, using duration as an independent variable was not very effective in many cases. In contrast, using maximum team size as an independent variable was effective when the error rate is equal or less than 50%.

DOI： 10.1007/978-3-319-62048-0_7

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© 2017 IEEE. In this research, a tool for evaluating algorithmic thinking ability of the primary schoolchildren was developed. This tool is based on the three categories of operations used to construct algorithms, namely, sequential operations, conditional branching operations, and iterative operations. Each question in the tool checks to see if the examinee understands the concept of one of the three categories. The tool was developed to evaluate the educational effect of programming education for middle to upper grade (third to sixth grade) primary schoolchildren. Since both Visual Programming Language (VPL) and Textual Programming Language (TPL) could be used, it was required that the tool could be used by both the group of children who use VPLs and the group of children who use TPLs. To make it possible, no programming language appeared in the questions in the tool. The teaching materials for the programming education were also developed in such a way that the three basic concepts of algorithm, namely, sequential processing, conditional branching, and repetitive processing, were clearly taught. The target VPL in this research was Scratch. The evaluation tool was conducted in a weekend class of programming education for primary schoolchildren, and the algorithmic thinking ability of the schoolchildren was analyzed.

DOI： 10.1109/FIE.2017.8190609

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Language：English   Publisher：IEEE Computer Society  

Context: Recent studies have shown that performance of defect prediction models can be affected when data sampling approaches are applied to imbalanced training data for building defect prediction models. However, the magnitude (degree and power) of the effect of these sampling methods on the classification and prioritization performances of defect prediction models is still unknown. Goal: To investigate the statistical and practical significance of using resampled data for constructing defect prediction models. Method: We examine the practical effects of six data sampling methods on performances of five defect prediction models. The prediction performances of the models trained on default datasets (no sampling method) are compared with that of the models trained on resampled datasets (application of sampling methods). To decide whether the performance changes are significant or not, robust statistical tests are performed and effect sizes computed. Twenty releases of ten open source projects extracted from the PROMISE repository are considered and evaluated using the AUC, pd, pf and G-mean performance measures. Results: There are statistical significant differences and practical effects on the classification performance (pd, pf and G-mean) between models trained on resampled datasets and those trained on the default datasets. However, sampling methods have no statistical and practical effects on defect prioritization performance (AUC) with small or no effect values obtained from the models trained on the resampled datasets. Conclusions: Existing sampling methods can properly set the threshold between buggy and clean samples, while they cannot improve the prediction of defect-proneness itself. Sampling methods are highly recommended for defect classification purposes when all faulty modules are to be considered for testing.

DOI： 10.1109/ESEM.2017.50

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J-GLOBAL

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Language：Japanese  

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Language：Japanese   Publisher：プロジェクトマネジメント学会  

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J-GLOBAL

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Language：English   Publisher：WORLD SCIENTIFIC PUBL CO PTE LTD  

Bug reports are one of the most crucial information sources for software engineering offering answers to many questions. Yet, getting these answers is not always easy; the information in bug reports is often implicit and some processes are required to extract the meaning of these reports. Most research in this area employ a supervised learning approach to classify bug reports so that required types of reports could be identified. However, this approach often requires an immense amount of time and effort, the resources that already too scarce in many projects.We aim to develop an automated framework that can categorize bug reports, according to their grammatical structure without the need for labeled data.Our framework categorizes bug reports according to their text similarity using topic modeling and a clustering algorithm. Each group of bug reports are labeled with our new clustering labeling algorithm specifically made for clusters in the topic space. Our framework is highly customizable with a modular approach and options to incorporate available background knowledge to improve its performance, while our cluster labeling approach make use of natural language process (NLP) chunking to create the representative labels.Our experiment results demonstrate that the performance of our unsupervised framework is comparable to a supervised learning one. We also show that our labeling process is capable of labeling each cluster with phrases that are representative for that cluster's characteristics.Our framework can be used to automatically categorize the incoming bug reports without any prior knowledge, as an automated labeling suggestion system or as a tool for obtaining knowledge about the structure of the bug report repository.

DOI： 10.1142/S0218194016500352

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Language：English   Publisher：IEICE-INST ELECTRONICS INFORMATION COMMUNICATIONS ENG  

GitHub is a developers' social networking service that hosts a great number of open source software (OSS) projects. Although some of the hosted projects are growing and have many developers, most projects are organized by a few developers and face difficulties in terms of sustainability. OSS projects depend mainly on volunteer developers, and attracting and retaining these volunteers are major concerns of the project stakeholders. To investigate the population structures of OSS development communities in detail and conduct software analytics to obtain actionable information, we apply a demographic approach. Demography is the scientific study of population and seeks to identify the levels and trends in the size and components of a population. This paper presents a case study, investigating the characteristics of the population structures of OSS projects on GitHub, and shows population projections generated with the well-known cohort component method. We found that there are four types of population structures in OSS development communities in terms of experiences and contributions. In addition, we projected the future population accurately using a cohort component population projection method. This method predicts a population of the next period using a survival rate calculated from past population. To the best of our knowledge, this is the first study that applied demography to the field of OSS research. Our approach addressing OSS-related problems based on demography will bring new insights, since studying population is novel in OSS research. Understanding current and future structures of OSS projects can help practitioners to monitor a project, gain awareness of what is happening, manage risks, and evaluate past decisions.

DOI： 10.1587/transinf.2015EDP7363

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Language：English   Publisher：IEICE-INST ELECTRONICS INFORMATION COMMUNICATIONS ENG  

Analogy-based software effort estimation has gained a considerable amount of attention in current research and practice. Its excellent estimation accuracy relies on its solution adaptation stage, where an effort estimate is produced from similar past projects. This study proposes a solution adaptation technique named LSA-X that introduces an approach to exploit the potential of productivity factors, i.e., project variables with a high correlation with software productivity, in the solution adaptation stage. The LSA-X technique tailors the exploitation of the productivity factors with a procedure based on the Linear Size Adaptation (LSA) technique. The results, based on 19 datasets show that in circumstances where a dataset exhibits a high correlation coefficient between productivity and a related factor (r = 0.30), the proposed LSA-X technique statistically outperformed (95% confidence) the other 8 commonly used techniques compared in this study. In other circumstances, our results suggest using any linear adaptation technique based on software size to compensate for the limitations of the LSA-X technique.

DOI： 10.1587/transinf.2015EDP7237

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Language：English   Publisher：IEEE  

Effort-inconsistency is a situation where historical software project data used for software effort estimation (SEE) are contaminated by many project cases with similar characteristics but are completed with significantly different amount of effort. Using these data for SEE generally produces inaccurate results; however, an effective technique for its handling is yet made to be available. This study approaches the problem differently from common solutions, where available techniques typically attempt to remove every project case they have detected as outliers. Instead, we hypothesize that data inconsistency is caused by only a few deviant project cases and any attempt to remove those other cases will result in reduced accuracy, largely due to loss of useful information and data diversity. Filter-INC (short for Filtering technique for handling effort-INConsistency in SEE datasets) implements the hypothesis to decide whether a project case being detected by any existing technique should be subject to removal. The evaluation is carried out by comparing the performance of 2 filtering techniques between before and after having Filter-INC applied. The results produced from 8 real-world datasets together with 3 machine-learning models, and evaluated by 4 performance measures show a significant accuracy improvement at the confident interval of 95%. Based on the results, we recommend our proposed hypothesis as an important instrument to design a data preprocessing technique for handling effort-inconsistency in SEE datasets, definitely an important step forward in preprocessing data for a more accurate SEE model.

DOI： 10.1109/APSEC.2016.48

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DOI： 10.11309/jssst.33.2_139

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DOI： 10.11309/jssst.33.2_139

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This paper extends our previous study (letter paper), which quantifies the difficulty of program comprehension based on brain activation measured by NIRS (Near Infra-Red Spectroscopy) during source code reading. We performed controlled experiments with 20 subjects. 3 of 20 subjects could not complete the measurement. We found that: (1) 16 of 17 shows strong brain activation during reading of obfuscated program (binomial test, p < 0:01) and (2) subjective evaluation of difficulty is correlated with brain activation (Spearman's correlation coefficient=0.46, p < 0:01).

DOI： 10.11309/jssst.33.2_78

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DOI： 10.11309/jssst.33.2_78

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Language：English   Publisher：IEEE COMPUTER SOC  

To prioritize software quality assurance efforts, fault prediction models have been proposed to distinguish faulty modules from clean modules. The performances of such models are often biased due to the skewness or class imbalance of the datasets considered. To improve the prediction performance of these models, sampling techniques have been employed to rebalance the distribution of fault-prone and non-fault-prone modules. The effect of these techniques have been evaluated in terms of accuracy/geometric mean/F1-measure in previous studies; however, these measures do not consider the effort needed to fix faults. To empirically investigate the effect of sampling techniques on the performance of software fault prediction models in a more realistic setting, this study employs Norm(Popt), an effort-aware measure that considers the testing effort. We performed two sets of experiments aimed at (1) assessing the effects of sampling techniques on effort-aware models and finding the appropriate class distribution for training datasets (2) investigating the role of balanced training and testing datasets on performance of predictive models. Of the four sampling techniques applied, the over-sampling techniques outperformed the under-sampling techniques with Random Over-sampling performing best with respect to the Norm(Popt) evaluation measure. Also, performance of all the prediction models improved when sampling techniques were applied between the rates of (20-30)% on the training datasets implying that a strictly balanced dataset (50% faulty modules and 50% clean modules) does not result in the best performance for effort-aware models. Our results also indicate that performances of effort-aware models are significantly dependent on the proportions of the two types of the classes in the testing dataset. Models trained on moderately balanced datasets are more likely to withstand fluctuations in performance as the class distribution in the testing data varies.

DOI： 10.1109/COMPSAC.2016.144

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Language：English   Publisher：IEEE COMPUTER SOC  

Association rule mining discovers patterns of co-occurrences of attributes as association rules in a data set. The derived association rules are expected to be recurrent, that is, the patterns recur in future in other data sets. This paper defines the recurrence of a rule, and aims to find a criteria to distinguish between high recurrent rules and low recurrent ones using a data set for software defect prediction. An experiment with the Eclipse Mylyn defect data set showed that rules of lower than 30 transactions showed low recurrence. We also found that the lower bound of transactions to select high recurrence rules is dependent on the required precision of defect prediction.

DOI： 10.1109/ICIS.2016.7550867

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Language：English   Publisher：IEEE COMPUTER SOC  

Software birthmarks have been proposed as a method for enabling the detection of programs that may have been stolen by measuring the similarity between the two programs. A birthmark is created from each program by extracting its native characteristics. The birthmarks of the programs can then be compared. However, because the extracted birthmarks contain a large amount of information, a large amount of time is needed when using them to compare large programs. This paper describes our work to reduce this comparison time. Achieving faster comparisons will enable the evaluation of large programs and simplify the use of birthmarks. Specifically, our method creates hashes from conventional birthmark information using fuzzy hashing, and then measures the similarity of the programs using the obtained hash values. Using the proposed method, we achieved a major speed increase over the conventional birthmark method with distinction rates of over 90%. On the other hand, because preservation performance decreased substantially, the similarity threshold value needed to be lowered when using the proposed method.

DOI： 10.1109/ICIS.2016.7550868

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Language：English   Publisher：IEEE COMPUTER SOC  

Recently, information system operation becomes more important because of increasing size of information system and outsourcing the system operation. However, it is not easy for customers to judge the validity of the system operation cost. To provide information which helps the judgment, we analyzed factors which affect system operation cost. Working time of system operation service provider has the strong relationship to the cost. So, if customers know the working time, they estimate the cost properly. However, it is difficult for customers to know the working time generally. So, we assumed that customers estimate unit cost and working time, to speculate total operation cost roughly. To help the estimation, we analyzed factors affected working time and unit cost. The analysis results show that working time is settled based on the software size and the number of users, and the unit cost of the engineers increases when network range of the system is wide.

DOI： 10.1109/ICIS.2016.7550863

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Language：English   Publisher：IEEE COMPUTER SOC  

In a software development project, project management is indispensable, and effort estimation is one of the important factors on the management. To improve estimation accuracy, outliers are often removed from dataset used for estimation. However, the influence of the outliers to the estimation accuracy is not clear. In this study, we added outliers to dataset experimentally, to analyze the influence. In the analysis, we changed the percentage of outliers, the extent of outliers, variable including outliers, and location of outliers on the dataset. After that, effort was estimated using the dataset. In the experiment, the influence of outliers was not very large, when they were included in the software size metric, the percentage of outliers was 10%, and the extent of outliers was 100%.

DOI： 10.1109/ICIS.2016.7550865

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To prioritize quality assurance efforts, various fault prediction models have been proposed. However, the best performing fault prediction model is unknown due to three major drawbacks: (1) comparison of few fault prediction models considering small number of data sets, (2) use of evaluation measures that ignore testing efforts and (3) use of n-fold cross validation instead of the more practical cross-release validation. To address these concerns, we conducted cross-release evaluation of 11 fault density prediction models using data sets collected from 2 releases of 25 open source software projects with an effort-aware performance measure known as Norm(P-opt). Our result shows that, whilst M5 and K* had the best performances; they were greatly influenced by the percentage of faulty modules present and size of data set. Using Norm(P-opt) produced an overall average performance of more than 50% across all the selected models clearly indicating the importance of considering testing efforts in building fault-prone prediction models.

DOI： 10.1109/QRS.2016.33

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The purpose of this research is to compare textual programming languages and visual programming languages from the aspect of motivation. As a textual programming language, Processing programming language was used, and as visual programming languages, Scratch, a derivation of Scratch, Teaching materials offered by code.org, and LEGO Mindstorms EV3 were used. Teaching materials using the textual programming language, and those using the visual programming languages were developed separately. A trial experiment of programming education with the textual programming language was conducted to a cohort of seven primary schoolchildren. Trial experiments with the visual programming languages were conducted twice. In each of them, a cohort of eight primary schoolchildren participated. The motivation of the children was assessed using the questionnaire based on the ARCS (Attention, Relevance, Confidence, and Satisfaction) motivation model. The results with the visual programming languages suggested that the motivation scores of the children increased as the class progressed when visual programming languages were used. On the other hand, the results with Processing suggested that the variance of Satisfaction factor increased as the class progressed when textual programming languages were used, which further suggested that the Satisfaction scores of the children spread as the class progressed when textual programming languages were used.

DOI： 10.1109/FIE.2016.7757571

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DOI： 10.1587/transinf.2016EDe0002

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In this paper, we propose a pinpoint-hide defense method, which aims to improve the stealth of obfuscated code. In the pinpointing process, we scan the obfuscated code in a few small code fragment level and identify all surprising fragments, that is, very unusual fragments which may draw the attention of an attacker to the obfuscated code. In the hiding process, we transform the pinpointed surprising fragments into unsurprising ones while preserving semantics. The obfuscated code transformed by our method consists only by unsurprising code fragments, therefore is more difficult for attackers to be distinguished from unobfuscated code than the original. In the case study, we apply our pinpoint-hide method to some programs transformed by well-known obfuscation techniques. The result shows our method can pinpoint surprising fragments such as dummy code that does not fit in the context of the program, and instructions used in a complicated arithmetic expression. We also confirm that instruction camouflage can make the pinpointed surprising fragments unsurprising ones, and that it runs correctly.

DOI： 10.1145/2843859.2843862

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Large software projects such as Mozilla Firefox and Eclipse own more than ten thousand bug reports that have been reported but left unresolved. To utilize such a great amount of unresolved bug reports and accelerate bug detection and removal, we propose to a way recommend programmers a bug report that is likely to contain failure descriptions related to a source file being inspected. We employ the vector space model (VSM) to make a relevancy ranking of bug reports to a given source file. The result of an experiment using data of three open source software projects showed that the accuracies of recommendations ranged from 21.74% to 60.05% in terms of the percentage of recommendations that contained relevant bug reports in a top 10 recommended list.

DOI： 10.1109/SWAN.2015.7070481

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In this research, a Textual Programming Language (TPL) is used in programming education for primary schoolchildren because of the following reasons: (1) it is more practical to use the programming languages similar to the ones used for developing real applications, (2) typing statements could be easier for primary schoolchildren than generally thought, (3) there exist programming environments such as Processing that are easy to use and produce very attractive graphical outcomes. Teaching material for programming education with Processing was developed. In this teaching material, cartoons were used to explain difficult concepts. The learners who use this teaching material were supposed to draw some computational figures with chosen colors. Trial experiments of programming education using this teaching material was conducted to a cohort of seven primary schoolchildren (six 4th grade and one 5th grade children) in two consecutive weekend classes (one hour each). Since the authors' aim of this programming education was to create a sense of fun and excitement in the children and inculcate a desire to engage with computing, the motivation of the children was assessed using the questionnaire based on the ARCS (Attention, Relevance, Confidence, and Satisfaction) motivation model. The results were encouraging and suggested that TPLs could be used in programming education for primary schoolchildren.

DOI： 10.1109/FIE.2015.7344187

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DOI： 10.1145/2745802.2745820

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This paper proposes a method for evaluating the artificiality of protected code by means of an N-gram model. The proposed artificiality metric helps us measure the stealth of the protected code, that is, the degree to which protected code can be distinguished from unprotected code. In a case study, we use the proposed method to evaluate the artificiality of programs that are transformed by well-known obfuscation techniques. The results show that static obfuscating transformations (e.g., control flow flattening) have little effect on artificiality. However, dynamic obfuscating transformations (e.g., code encryption), or a technique that inserts junk code fragments into the program, tend to increase the artificiality, which may have a significant impact on the stealth of the code.

DOI： 10.1109/SPRO.2015.14

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Software maintenance is an important activity on the software lifecycle. Software maintenance does not mean only removing faults found after software release. Software needs extensions or modifications of its functions due to changes in a business environment, and software maintenance also indicates them. In this research, we try to establish a benchmark of work efficiency for software maintenance. To establish the benchmark, factors affecting work efficiency should be clarified, using a dataset collected from various organizations (cross-company dataset). We used dataset includes 134 data points collected by Economic Research Association in 2012, and analyzed factors affected work efficiency of software maintenance. We defined the work efficiency as number of modified modules divided by working time. The main contribution of our research is illustrating factors affecting work efficiency, based on the analysis using cross-company dataset and working time. Also, we showed work efficiency, classified the factor. It can be used to benchmark an organization. We empirically illustrated that using Java and restriction of development tool affect to work efficiency.

DOI： 10.1109/ACIT-CSI.2015.13

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Analogy-based estimation (ABE) is one of the most time consuming and compute intensive method in software de- velopment effort estimation. Optimizing ABE has been a dilemma because simplifying the procedure can reduce the estimation performance, while increasing the procedure com- plexity with more sophisticated theory may sacrifice an ad- vantage of the unlimited scalability for a large data input. Motivated by an emergence of cloud computing technology in software applications, in this study we present 3 different implementation schemes based on Hadoop MapReduce to optimize the ABE process across multiple computing in- stances in the cloud-computing environment. We experimentally compared the 3 MapReduce implementation schemes in contrast with our previously proposed GPGPU approach (named ABE-CUDA) over 8 high-performance Amazon EC2 instances. Results present that the Hadoop solution can pro- vide more computational resources that can extend the scalability of the ABE process. We recommend adoption of 2 different Hadoop implementations (Hadoop streaming and RHadoop) for accelerating the computation specifically for compute-intensive software engineering related tasks.

DOI： 10.1145/2666581.2666582

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Language：Japanese   Publisher：一般社団法人情報処理学会  

近年，情報システムの規模の増大や，システム運用の外部委託の進展に伴い，システム運用に関する注目が高まっている．システム運用費用が妥当であるかどうかは，システム運用の委託側企業にとって判断が難しい．本稿では，委託側企業がシステム運用費用の妥当性判断の参考となるような情報の提供を目指し，システム運用費用に影響を与える要因の分析を行った．受託側の作業時間と運用費用は非常に関連が強いため，受託側作業時間を把握することができれば，標準的な運用費用を推定することができる．ただし，受託側作業時間を委託側企業が把握することは一般に容易ではない．そこで本稿では，作業時間と技術者の単価から簡易的に価格を推定することを前提とし，作業時間と単価に影響する要因を個別に分析した．その結果，作業時間はプログラム本数と最大利用者数から決まることや，ネットワーク範囲が狭い場合，単価が低くなる傾向があることがわかった．

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Language：Japanese   Publisher：一般社団法人電子情報通信学会  

近年,受託開発ソフトウェアは新規の開発が頭打ちの状況に対して,開発済みのソフトウェア資産が増えることに伴い保守の比率が高まり,保守の重要性が高まっている.このため,保守単独で契約を結ぶ場合もある.保守とは,単にソフトウェア出荷後に発見された欠陥を除去することだけを指すのではない.ソフトウェアは利用されているうちに,ビジネス環境の変化により,機能の拡張,修正が必要となる.保守とはそれらの修正も含んだ概念である.本研究では,ソフトウェア保守における作業効率のベンチマークを確立することを試みる.ベンチマークを確立するためには,複数の組織から収集されたプロジェクトのデータ,すなわち企業横断的データを用いて,作業効率に影響する特性を明らかにする必要がある.本研究では,企業横断的データとして,一般財団法人経済調査会によって平成24年度に収集された134件の事例を用い,作業効率に影響する要因を分析した.作業効率は修正実施プログラム本数÷作業時間数と定義した.分析では,ベンチマーキングのために,箱ひげ図により各特性の作業効率の分布を示すとともに,開発言語がJavaであるかどうか,ツールの制約が強いかどうかが作業効率に影響することを示した.

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Language：Japanese   Publisher：一般社団法人情報処理学会  

近年，受託開発ソフトウェアは新規の開発が頭打ちの状況に対して，開発済みのソフトウェア資産が増えることに伴い保守の比率が高まり，保守の重要性が高まっている．このため，保守単独で契約を結ぶ場合もある．保守とは，単にソフトウェア出荷後に発見された欠陥を除去することだけを指すのではない．ソフトウェアは利用されているうちに，ビジネス環境の変化により，機能の拡張，修正が必要となる．保守とはそれらの修正も含んだ概念である．本研究では，ソフトウェア保守における作業効率のベンチマークを確立することを試みる．ベンチマークを確立するためには，複数の組織から収集されたプロジェクトのデータ，すなわち企業横断的データを用いて，作業効率に影響する特性を明らかにする必要がある．本研究では，企業横断的データとして，一般財団法人経済調査会によって平成 24 年度に収集された 134 件の事例を用い，作業効率に影響する要因を分析した．作業効率は修正実施プログラム本数 ÷ 作業時間数と定義した．分析では，ベンチマーキングのために，箱ひげ図により各特性の作業効率の分布を示すとともに，開発言語が Java であるかどうか，ツールの制約が強いかどうかが作業効率に影響することを示した．

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Language：Japanese   Publisher：一般社団法人情報処理学会  

本稿はソフトウェア開発者の能力向上・プロセス改善手法である PSP における作業履歴の記録と作業目的に基づいた履歴の分析を支援するために，作業者の作業履歴から作業目的を予測する手法を提案する．提案手法は作業目的が頻繁に変化しないという仮説の元，予測対象となる作業 (Task) の前後に行われた作業の特性から作業目的 (Aim) を推定する．連続する Task のうち，打鍵数やクリック数が多い Task をその Aim における主要 Task とし，特性として使用する．RandomForest による予測モデルを構築し実験した結果，提案手法は時系列情報を用いない場合に比べ，高い精度で作業を予測できた．特に，前後の作業を多く見るほど予測精度の向上が見られた．

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In the past, the authors had been analyzing motivation of the learners in programming education using the ARCS assessment metric. This metric had been used in the application experiment in 13 programming courses, and about 1,700 sets of data was collected. From these data, the learners' model, characteristics of the change of motivation, and ways of improving teaching materials had been clarified. However, these study results were obtained after the terms, when the programming courses were over, and thus did not contribute much to the ongoing programming education. For this reason, in this research, the methods for predicting the change of learners' motivation were studied so that the learners who may need support could be identified. The idea came from the experiment the authors conducted, in which the motivation of learners was analyzed by plotting the motivation scores of each factor in the ARCS model as a 3D graph. As a result, a decreasing tendency of motivation was observed when the distribution of the plot widened. After studying the tendency in detail, it was thought to be due to the influence of the variance of sub-level category scores. In the proposed method, the motivation of each learner is assessed in each lesson using the ARCS assessment metric. If variance of the motivation scores of a learner in a lesson is above a certain threshold value AND if mean of the scores has not decreased from the previous lesson, then the learner is identified as a candidate of learner who needs support at that lesson. In the application experiment, a programming course with 9 lessons was offered and 9 learners attended all the 9 lessons. In the experiment, 7 cases had been identified as the candidates of learners who need support, and out of those 7 cases, a decrease of motivation to less than average was observed in 5 cases.

DOI： 10.1109/FIE.2014.7044221

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To date, various techniques for predicting fault-prone modules have been proposed; however, test strategies, which assign a certain amount of test effort to each module, have been rarely studied. This paper proposes a simulation model of software testing that can evaluate various test strategies. The simulation model estimates the number of discoverable faults with respect to the given test resources, the test strategy, complexity metrics of a set of modules to be tested, and the fault prediction results. Based on a case study of simulation applying fault prediction to two open source software (Eclipse and Mylyn), we show the relationship between the available test effort and the effective test strategy.

DOI： 10.11309/jssst.31.2_118

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Language：English   Publisher：IEEE  

As open source software (OSS) has become an integral part of today's software businesses, many software companies rely on OSS to develop their customer solutions and products. On the other hand, they face various concerns in using OSS, such as technical support, quality, security and licensing issues. This paper focuses on OSS-related FAQ in industry, and tries to answer them or to provide research directions based on lessons learned from recent mining OSS repository researches.

DOI： 10.1109/IWESEP.2014.12

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Language：English   Publisher：IEEE  

Background: Information in bug reports is implicit and therefore difficult to comprehend. To extract its meaning, some processes are required. Categorizing bug reports is a technique that can help in this regard. It can be used to help in the bug reports management or to understand the underlying structure of the desired project. However, most researches in this area are focusing on a supervised learning approach that still requires a lot of human afford to prepare a training data. Aims: Our aim is to develop an automated framework than can categorize bug reports, according to their hidden characteristics and structures, without the needed for training data. Method: We solve this problem using clustering, unsupervised learning approach. It can automatically group bug reports together based on their textual similarity. We also propose a novel method to label each group with meaningful and representative names. Results: Experiment results show that our framework can achieve performance comparable to the supervised learning approaches. We also show that our labeling process can label each cluster with representative names according to its characteristic. Conclusion: Our framework could be used as an automated categorization system that can be applied without prior knowledge or as an automated labeling suggestion system.

DOI： 10.1109/IWESEP.2014.8

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DOI： 10.11309/jssst.31.2_118

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Language：English   Publisher：ASSOC COMPUTING MACHINERY  

Program comprehension is a fundamental activity in software development that cannot be easily measured, as it is performed inside the human brain. Using a wearable Near Infra-red Spectroscopy (NIRS) device to measure cerebral blood flow, this paper tries to answer the question: Can the measurement of brain blood-flow quantify programmers' mental workload during program comprehension activities? We performed a controlled experiment with 10 subjects; 8 of them showed high cerebral blood flow while understanding strongly obfuscated programs (requiring high mental workload). This suggests the possibility of using NIRS to measure the mental workload of a person during software development activities.

DOI： 10.1145/2591062.2591098

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Language：Japanese   Publisher：Japan Society for Software Science and Technology  

In this research, we aim to quantify the difficulty of program comprehension during source code reading. We use Near Infra-Red Spectroscopy(NIRS) to measure the activation of brain. As a result of an experiment with 10 subjects, 8 of them showed a strong activation in the brain during reading of strongly obfuscated programs that are extremely difficult to comprehend. We also normalized the data for each participant and aggregated them for statistical testing. As a result of t-test, significant difference (p < 0.001) was seen in the mean of the brain blood flow between obfuscated and non-obfuscated programs.

DOI： 10.11309/jssst.31.3_270

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Language：Japanese   Publisher：Japan Society for Software Science and Technology  

In this research, we aim to quantify the difficulty of program comprehension during source code reading. We use Near Infra-Red Spectroscopy(NIRS) to measure the activation of brain. As a result of an experiment with 10 subjects, 8 of them showed a strong activation in the brain during reading of strongly obfuscated programs that are extremely difficult to comprehend. We also normalized the data for each participant and aggregated them for statistical testing. As a result of t-test, significant difference (p < 0.001) was seen in the mean of the brain blood flow between obfuscated and non-obfuscated programs.

DOI： 10.11309/jssst.31.3_270

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Language：English   Publisher：IEEE COMPUTER SOC  

Until now, various techniques for predicting fault-prone modules have been proposed and evaluated in terms of their prediction performance; however, their actual contribution to business objectives such as quality improvement and cost reduction has rarely been assessed. This paper proposes using a simulation model of software testing to assess the cost effectiveness of test effort allocation strategies based on fault prediction results. The simulation model estimates the number of discoverable faults with respect to the given test resources, the resource allocation strategy, a set of modules to be tested, and the fault prediction results. In a case study applying fault prediction of a small system to acceptance testing in the telecommunication industry, results from our simulation model showed that the best strategy was to let the test effort be proportional to "the number of expected faults in a module x log(module size)." By using this strategy with our best fault prediction model, the test effort could be reduced by 25 percent while still detecting as many faults as were normally discovered in testing, although the company required about 6 percent of the test effort for metrics collection, data cleansing, and modeling. The simulation results also indicate that the lower bound of acceptable prediction accuracy is around 0.78 in terms of an effort-aware measure, Norm(P-opt). The results indicate that reduction of the test effort can be achieved by fault prediction only if the appropriate test strategy is employed with high enough fault prediction accuracy. Based on these preliminary results, we expect further research to assess their general validity with larger systems.

DOI： 10.1109/TSE.2013.21

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Language：Japanese   Publisher：一般社団法人情報処理学会  

数学的モデルに基づく工数見積もりでは，開発するソフトウェアの規模と生産性要因を説明変数として工数を見積もる．いくつかの先行研究では，開発要員数が工数見積もりモデルの説明変数として用いられている．要員数はプロジェクトの生産性との関連が強いことが指摘されており，要員数を説明変数として用いることにより，モデルの見積もり精度向上が期待できる．ただし，工数見積もり時における要員数は見積もり値であり，実績の要員数との誤差が生じることにより見積もり精度が低下する可能性がある．そこで本稿では要員数を用いた新たな見積もり方法を提案する．提案方法では，要員数が過去プロジェクト平均と比較して非常に小さいと見積もられた場合，要員数が小さいことを前提とするモデルを用いる．逆に，要員数が非常に大きいと見積もられた場合，要員数が大きいことを前提とするモデルを用いる．これらに当てはまらない場合，要員数を説明変数に含まないモデルを用いて工数を見積もる．評価実験において，要員数の誤差を実験的に変化させ，提案方法の見積もり精度を評価した．その結果，提案方法は要員数の誤差に対してロバストであり，特に要員数の誤差が不明な場合や誤差が大きい場合に有効に働くことがわかった．

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昨今，ソフトウェア開発のコスト削減を目的として，商用ソフトウェアの一部にオープンソースソフトウェア (OSS) を利用する企業が増加している．OSS 導入時には，対象プロジェクトのリリース後における保守体制を理解することが重要であり，OSS 開発の不具合修正プロセスを分析する研究が進んでいる．不具合修正プロセス中のコミッターによるレビュー作業は，ソフトウェアをリリースする直前の作業であり，ソフトウェアの品質に最も影響を与える．したがって，コミッターの不十分なレビュー作業は開発の手戻り （再修正） を引き起こし作業効率の低下に繋がる．そこで本稿はコミッターのレビュー作業による OSS 品質への影響について考察する．

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Language：Japanese   Publisher：The Institute of Electronics, Information and Communication Engineers  

To prioritize testing effort, it is important to predict fault-prone modules that are likely to contain a fault To build a better fault-prone module prediction model, This paper proposes two methods (module classifica-tion and module selection) to produce a fit dataset (for model building) that has smulai aspects to the prediction target dataset so that resultant model may have a good prediction performance when it is applied to the target dataset By using Eclipse dataset, we experimentally evaluate the proposed methods.

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The Mining Software Repositories (MSR) is an attractive research field that has tons of research topics. It is great fun for a researcher to mine new research goals from software repositories. The MSR field has been grown up with OSS communities, as researchers have mined repositories of various open source software (OSS) projects until now. In this paper we introduce fun of the MSR field as well as its typical research topics, example of mining and future directions.

DOI： 10.11309/jssst.30.2_52

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To improve tamper resistance of programs against illegal modification, this paper proposes instruction folding applicable to Java platform. In the proposed method, at first, similar methods are selected in a Java program. Next, these methods are merged into one method and diffs among these methods are stored in the program. Then, at runtime, when one of the merged methods is executed, diffs are restored by self-modification, which is realized by the Java instrumentation mechanism. The proposed method is resilient against tampering of folded method. Even if an adversary modifies the folded method, the program goes crash because the method is repeatedly modified at runtime. © 2013 IEEE.

DOI： 10.1109/SNPD.2013.31

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This paper quantifies the difficulty of program analysis based on the information theory. The basic idea is to consider that a program is ultimately obfuscated if instructions appear at random
that is, (1) all instructions has an equal frequency of appearance, and (2) there is no pattern observed in the instruction sequence. We quantified (1) based on the entropy and (2) based on the Kolmogorov complexity. We evaluated the feasibility of our proposal through a case study. © 2013, Japan Society for Software Science and Technology. All rights reserved.

DOI： 10.11309/jssst.30.3_18

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We propose a hybrid effort estimation method based on the multivariate liner regression analysis and the analogy based estimation method (ABE). First, our method calculates the unreliability index of ABE on an estimation target project. Next, our method selects log-log regression estimation when the value of the index is low. Otherwise our method selects ABE estimation or combined estimation (the average of ABE and log-log regression estimation). In the experiment, we compared estimation accuracies of our method with conventional methods, and the results showed that the median of Balanced Relative Error (estimation accuracy index) was improved from 47.2% to 39.7%, when the variance of similar projects' effort was used as the reliability index.

DOI： 10.11309/jssst.30.2_227

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In open source software development, the collaboration among developers is the key to improve software quality. In particular, to fix a bug related to various parts of a system, developers need collaboration because each developer usually has very limited knowledge about a large software system. This paper aims to clarify how narrow (or how wide) is each developer's knowledge area in the Eclipse project, and how often do developers need to collaborate with each other. As a result of analysis, we found that 50 % of committers take care of just one or two modules, which indicates the necessity of collaboration when a bug-fix affects multiple modules. In addition, we also found the significant relationship between committers' collaborations and the re-opened bugs. We conclude that a committer should be aware the risk of re-opened bugs caused by the collaboration. Copyright 2013 ACM.

DOI： 10.1145/2501535.2501539

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Language：English   Publisher：IEEE  

In this paper, a method for improving the teaching materials of programming education is introduced, and the evaluation of the effects of using the strategy is presented. By using this method, the teachers of programming education will be able to assess and improve their teaching materials irrespective of their knowledge and experience of their teaching materials already used. In this method, the teaching materials were improved based on the statistical analysis of the motivation of students. Specifically, the motivation of students was measured for each lower category of ARCS motivation model with the authors' original questionnaire. The lower category in a particular lesson that showed a statistically significant decrease from the previous lesson was identified, and the improvement strategies for the lower category were selected from the list of motivation strategies in the ARCS model. The teaching materials of programming education were then improved based on the strategy. In this research, five lower categories of particular lessons in a programming course were identified, and the teaching materials were improved. The improved teaching materials were used in the following programming course, and the effects of the improvements were seen in three lower categories out of the identified five lower categories.

DOI： 10.1109/FIE.2013.6684920

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Language：English   Publisher：IEEE COMPUTER SOC  

In an Open Source Software (OSS) project, many developers contribute by submitting source code patches. To maintain the quality of the code, certain experienced developers review each patch before it can be applied or committed. Ideally, within a short amount of time after its submission, a patch is assigned to a reviewer and reviewed. In the real world, however, many large and active OSS projects evolve at a rapid pace and the core developers can get swamped with a large number of patches to review. Furthermore, since these core members may not always be available or may choose to leave the project, it can be challenging, at times, to find a good reviewer for a patch. In this paper, we propose a graph-based method to automatically recommend the most suitable reviewers for a patch. To evaluate our method, we conducted experiments to predict the developers who will apply new changes to the source code in the Eclipse project. Our method achieved an average recall of 0.84 for top-5 predictions and a recall of 0.94 for top-10 predictions.

DOI： 10.1109/APSEC.2013.103

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Language：English   Publisher：IEEE COMPUTER SOC  

The performance of software cost estimation based on analogy reasoning depends upon the measures that specifying the similarity between software projects. This paper empirically investigates the use of probabilistic-based distance functions to improve the similarity measurement. The probabilistic-based distance functions are considerably more robust, because they collect the implicit correlation between the occurrences of project feature attributes. This information gain enables the constructed estimation model to be more concise and comprehensible. The study compares 6 probabilistic-based distance functions against the commonly-used Euclidian distance. We empirically evaluate the implemented cost estimation model using 5 real-world datasets collected from the PROMISE repository. The result shows a significant improvement in terms of error reduction, that implies an estimation based on probabilistic-based distance functions achieve higher accuracy on average, and the peak performance significantly outperforms the Euclidian distance based on Wilcoxon signed-rank test.

DOI： 10.1109/APSEC.2013.78

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Language：English   Publisher：IEICE-INST ELECTRONICS INFORMATION COMMUNICATIONS ENG  

This paper customizes Goal/Question/Metric (GQM) project monitoring models for various projects and organizations to take advantage of the data from the software tool EPM and to allow the tailoring of the interpretation models based upon the context and success criteria for each project and organization. The basic idea is to build less concrete models that do not include explicit baseline values to interpret metrics values. Instead, we add hypothesis and interpretation layers to the models to help people of different projects make decisions in their own context. We applied the models to two industrial projects, and found that our less concrete models could successfully identify typical problems in software projects.

DOI： 10.1587/transinf.E95.D.2169

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Language：Japanese  

ソフトウェア開発における工数予測は，プロジェクト完遂に必要な人的資源やスケジュール管理を行う上で重要である．工数予測手法には，過去のソフトウェア開発プロジェクトの実績データに基づいて，工数予測モデルを構築する手法がある．我々は，工数予測モデルの中で広く用いられている対数重回帰分析によるモデル （対数重回帰モデル） に着目した．対数重回帰モデルは，オーバーフィッティングが発生する可能性があるという問題がある．そこで本稿では，オーバーフィッティングに考慮した説明変数のランダムサンプリングを用いた対数重回帰モデルによるソフトウェア開発工数予測手法を提案する．Effort estimation is important for project managers in controlling human resources in a software project. There is the method which builds an effort estimation model based on software measurement data. We focused the model by multivariate log-log regression analysis, multivariate log-log regression model that is widely used to predict effort. Multivariate log-log regression model have the annoying problem of over-fitting. In this paper, We propose the technique of performing effort prediction using multivariate log-log regression model which takes the problem of over-fitting into consideration.

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Language：Japanese  

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DOI： 10.11309/jssst.29.3_157

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Language：English   Publisher：IEEE  

In the beginning of a contracted based software development project, the RFP is provided by a software user company and used as an initial system requirements specification to ask software developer companies to propose their technical plans to fulfill the requirements. In this process, it is very important to evaluate the quality of the RFP to make sure that basic user requirements are written enough. Especially, nonfunctional requirements (NFRs) are important since the system architecture greatly depends on the NFRs such as response time and security issues. This paper proposes a simple evaluation model of NFRs included in the RFP, mainly focusing on the user maintenance and operation issues. This model consists of NFR categories, NFR metrics, description level grading and weight to each NFR. As a case study, RFPs of 29 projects were evaluated by the proposed model. As a result, we confirmed that the model could identify poorly-written NFR aspects in the RFP, which need refinement before asking the developer company for a proposal.

DOI： 10.1109/IWSM-MENSURA.2012.23

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Language：English   Publisher：IEEE COMPUTER SOC  

Background: Association rules are more comprehensive and understandable than fault-prone module predictors (such as logistic regression model, random forest and support vector machine). One of the challenges is that there are usually too many similar rules to be extracted by the rule mining.
Aim: This paper proposes a rule reduction technique that can eliminate complex (long) and/or similar rules without sacrificing the prediction performance as much as possible.
Method: The notion of the method is to removing long and similar rules unless their confidence level as a heuristic is high enough than shorter rules. For example, it starts with selecting rules with shortest length (length=1), and then it continues through the 2nd shortest rules selection (length=2) based on the current confidence level, this process is repeated on the selection for longer rules until no rules are worth included.
Result: An empirical experiment has been conducted with the Mylyn and Eclipse PDE datasets. The result of the Mylyn dataset showed the proposed method was able to reduce the number of rules from 1347 down to 13, while the delta of the prediction performance was only .015 (from .757 down to .742) in terms of the F1 prediction criteria. In the experiment with Eclipsed PDE dataset, the proposed method reduced the number of rules from 398 to 12, while the prediction performance even improved (from .426 to .441.)
Conclusion: The novel technique introduced resolves the rule explosion problem in association rule mining for software proneness prediction, which is significant and provides better understanding of the causes of faulty modules.

DOI： 10.1109/APSEC.2012.103

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Language：English   Publisher：IEEE COMPUTER SOC  

One of the common problems in building an effort estimation model is that not all the effort factors are suitable as predictor variables. As a supplement of missing information in estimation models, this paper explores the project manager's knowledge about the target project. We assume that the experts can judge the target project's productivity level based on his/her own expert knowledge about the project. We also assume that this judgment can be further improved, because using the expert's judgment solely could incur subjective perception. This paper proposes a regression model building/selection method to address this challenge. In the proposed method, a fit dataset for model building is divided into two or three subsets by project productivity, and an estimation model is built on each data subset. The expert judges the productivity level of the target project and selects one of the models to be used. In the experiment, we used three datasets to evaluate the produced effort estimation models. In the experiment, we adjusted the error rate of the judgment and analyzed the relationship between the error rate and the estimation accuracy. As a result, the judgment-incorporating models produced significantly higher estimation accuracy than the conventional linear regression model, where the expert's error rate is less than 37%.

DOI： 10.1109/APSEC.2012.58

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Language：English   Publisher：IEEE COMPUTER SOC  

Open source software projects (OSS) receive a large number of bug reports from various contributors and developers alike, where many planned to be fixed by OSS developers. Given the next release cycle information, OSS users can be more effective and flexible in planning and to fix the bugs that are not to be fixed in the next release. It is therefore vital for OSS users to learn which bugs the OSS developers will fix, unfortunately such information may not be readily available, nor there is a prediction framework exists to serve such an important purpose. In this study, we would like to answer the question "Will this bug be fixed by the next release?", this is addressed by building a bug fixing prediction model based on the characteristics of a bug-related metric and by incorporating the progress of bug fixing measures such as status, period and developer metrics to provide aggregated information for the OSS users. The proposed model calculates the deviance of each variable to analyze the most important metrics, and it has been experimented using a case study with Eclipse platform. Result shows a bug fixing prediction model using both base metrics and state metrics provide significantly better performance in precision (139%) and recall (114%) than the standard model using only base metrics.

DOI： 10.1109/APSEC.2012.86

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Language：Japanese   Publisher：経済調査会  

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Background: Accurate effort estimation is the basis of the software development project management. The linear regression model is one of the widely-used methods for the purpose. A dataset used to build a model often includes categorical variables denoting such as programming languages. Categorical variables are usually handled with two methods: the stratification and dummy variables. Those methods have a positive effect on accuracy but have shortcomings. The other handing method, the interaction and the hierarchical linear model (HLM), might be able to compensate for them. However, the two methods have not been examined in the research area. Aim: giving useful suggestions for handling categorical variables with the stratification, transforming dummy variables, the interaction, or HLM, when building an estimation model. Method: We built estimation models with the four handling methods on ISBSG, NASA, and Desharnais datasets, and compared accuracy of the methods with each other. Results: The most effective method was different for datasets, and the difference was statistically significant on both mean balanced relative error (MBRE) and mean magnitude of relative error (MMRE). The interaction and HLM were effective in a certain case. Conclusions: The stratification and transforming dummy variables should be tried at least, for obtaining an accurate model. In addition, we suggest that the application of the interaction and HLM should be considered when building the estimation model.

DOI： 10.1145/2372251.2372267

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Language：English  

Price of custom software is very important for a user (end user company). However, there is very little information which helps the user judge the validity of the custom software price. So, one of our research goals is building price estimation model and showing its accuracy for the user to judge the validity of the custom software price. The other goal is how to get value for money custom software. In the analysis, we used 31 projects collected from Japanese organizations. First, we analyzed relationships of unit price of effort, unit price of FP (function point), and productivity. The analysis result showed productivity is more important variable than unit price of effort for the custom software price estimation. Next, relationships of other variables were analyzed to identify important variables for the price estimation. The result suggested some variables such as system architecture are important for that. At the end, the price was estimated based on FP and effort. In the analysis, when the price was estimated based on FP, the median balanced relative error (BRE) was 86.6%, and when it was estimated based on effort, median BRE was 20.2%. © 2012 IEEE.

DOI： 10.1109/IWESEP.2012.19

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In software development, prediction of fault-prone modules is an important challenge for effective software testing. However, high prediction accuracy may not be achieved in cross-project prediction, since there is a large difference in distribution of predictor variables between the base project and the target project.@In this paper we propose an prediction technique called gan ensemble of simple regression modelsh to improve the prediction accuracy of cross-project prediction. The proposed method uses weighted sum of outputs of simple logistic regression models to improve the generalization ability of logistic models. To evaluate the performance of the proposed method, we conducted cross-project prediction using datasets of projects from NASA IV&amp
V Facility Metrics Data Program. As a result, the proposed method outperformed conventional logistic regression models in terms of AUC of the Alberg diagram. © 2012 IEEE.

DOI： 10.1109/SNPD.2012.34

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