Faculty Profiles - MONDEN Akito

Data Smoothing for Software Effort Estimation Reviewed

Kent Korenaga, Akito Monden, Zeynep Yucel

2019 20TH IEEE/ACIS INTERNATIONAL CONFERENCE ON SOFTWARE ENGINEERING, ARTIFICIAL INTELLIGENCE, NETWORKING AND PARALLEL/DISTRIBUTED COMPUTING (SNPD) 501 - 506 2019.7

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.

On Preventing Symbolic Execution Attacks by Low Cost Obfuscation Reviewed

Toshiki Seto, Akito Monden, Zeynep Yücel, Yuichiro Kanzaki

Proc. 20th IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD 2019) 495 - 500 2019.7

Prediction of Software Defects Using Automated Machine Learning Reviewed

Kazuya Tanaka, Akito Monden, Zeynep Yucel

2019 20TH IEEE/ACIS INTERNATIONAL CONFERENCE ON SOFTWARE ENGINEERING, ARTIFICIAL INTELLIGENCE, NETWORKING AND PARALLEL/DISTRIBUTED COMPUTING (SNPD) 490 - 494 2019

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.

Proposal of Association Rule with Correlation Functions and Its Application to Software Development Data.

Hidekazu Saito, Akito Monden, Zeynep Yücel, Shuji Morisaki

Computer Software 36 ( 3 ) 47 - 53 2019

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

Effectiveness of auto-sklearn in Software Bug Prediction

Kazuya Tanaka, Akito Monden, Yucel Zeynep

Computer Software 36 ( 4 ) 46 - 52 2019

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

Introduction to the special issue on foundations of software engineering

Report on the 25th foundation of software engineering workshop (FOSE2018)

Kei Ito, Toshihiro Kamiya, Akito Monden

Computer Software 36 ( 4 ) 60 - 67 2019

Monden, A., Ito, K., Kamiya, T., Aman, H., Hanakawa, N., Sawada, A., Sugiyama, Y., Washizaki, H.

Computer Software 36 ( 4 ) 2 - 2 2019

シンボリック実行攻撃を妨げるソフトウェア難読化方法の検討

瀬戸俊輝, 門田暁人, 神崎雄一郎

ソフトウェア工学の基礎XXV，ソフトウェア工学の基礎ワークショップFOSE2018 145 - 146 2018.11

難読化されたJavaバイトコードに対するシンボリック実行攻撃の困難さ評価の検討

西陽太, 神崎雄一郎, 門田暁人, 玉田春昭

ソフトウェア工学の基礎XXV，ソフトウェア工学の基礎ワークショップFOSE2018 151 - 152 2018.11

Cross-validation-based association rule prioritization metric for software defect characterization Reviewed

Watanabe, T., Monden, A., Y{\"u}cel, Z., Kamei, Y., Morisaki, S.

IEICE Transactions on Information and Systems E101D ( 9 ) 2269 - 2278 2018.9

難読化されたプログラムの自動解析への耐性に関する考察

西陽太, 神崎雄一郎, 門田暁人

情報処理学会第80回全国大会講演論文集 (講演番号2J-04) 2018.3

分岐命令のカムフラージュに基づくプログラムの制御フローの隠ぺい

村上隼之助, 神崎雄一郎, 門田暁人

情報処理学会九州支部火の国情報シンポジウム2018 C3-3 2018.3

A feasibility study of two-step software effort estimation Reviewed

Naoki Kinoshita, Akito Monden

Computer Software 35 ( 1 ) 194 - 200 2018.2

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

An Attempt to Line-wise Bug Prediction Using Moving Window.

FUKUTANI Keigo, MONDEN Akito, ZEYNEP Yucel, HATA Hideaki

Computer Software 35 ( 4 ) 122 - 128 2018.1

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>

CiNii Article

Generation of Mimic Software Project Data Sets for Software Engineering Research. Reviewed

Kurtosis and Skewness Adjustment for Software Effort Estimation. Reviewed

Seiji Fukui, Akito Monden, Zeynep Yücel

25th Asia-Pacific Software Engineering Conference, APSEC 2018, Nara, Japan, December 4-7, 2018 504 - 511 2018

Maohua Gan, Kentaro Sasaki, Akito Monden, Zeynep Yücel

Proceedings of the 6th International Workshop on Quantitative Approaches to Software Quality co-located with 25th Asia-Pacific Software Engineering Conference (APSEC 2018), Nara, Japan, December 4, 2018. 2273 38 - 43 2018

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.

Scaling up software birthmarks using fuzzy hashing

Takehiro Tsuzaki, Teruaki Yamamoto, Haruaki Tamada, Akito Monden

International Journal of Software Innovation 5 ( 3 ) 89 - 102 2017.7

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

Examining Software Engineering Beliefs about System Testing Defects

Akito Monden, Masateru Tsunoda, Mike Barker, Kenichi Matsumoto

IT PROFESSIONAL 19 ( 2 ) 58 - 64 2017.3

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

Industry Application of Software Development Task Measurement System: TaskPit

Pawin Suthipornopas, Pattara Leelaprute, Akito Monden, Hidetake Uwano, Yasutaka Kamei, Naoyasu Ubayashi, Kenji Araki, Kingo Yamada, Ken-ichi Matsumoto

IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS E100D ( 3 ) 462 - 472 2017.3

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