研究者詳細 - 門田　暁人

MISC - 門田　暁人

分割表示 243 件中 41 - 60 件目／全件表示 >>

ソフトウェアエンジニアに求められる技術の求人票に基づく分析 (ソフトウェアサイエンス)

岩本寛伸, 門田暁人

電子情報通信学会技術研究報告 = IEICE technical report : 信学技報 116 ( 426 ) 89 - 94 2017年1月

COCOMO IIをベースとした工数見積りモデルの研究

大岩佐和子, 押野智樹, 門田暁人, 松本健一

プロジェクトマネジメント学会誌 19 ( 4 ) 53 - 58 2017年

An Attempt to Statement-level Software Bug Prediction

Fukutani, K, Monden, A, Yucel, Z, Hata, H

Proceedings of Fundamentals of Software Engineering, FOSE 2017 2017年

バイナリコード中の文字列に着目したソフトウェアの流用検出 (ソフトウェアサイエンス)

渡辺哲士, 門田暁人, 玉田春昭, 神崎雄一郎

電子情報通信学会技術研究報告 = IEICE technical report : 信学技報 116 ( 277 ) 79 - 84 2016年10月

原型分析を用いたソフトウェアバグ分析

瀧本恵介, 門田暁人, 尾上紗野, 畑秀明, 亀井靖高

電子情報通信学会技術研究報告 116 ( 277(SS2016 18-35) ) 91‐96 2016年10月

バイナリプログラム圧縮によるソフトウェア流用検出

上村康輔, 門田暁人, 畑秀明, 松本健一

電子情報通信学会技術研究報告 116 ( 277(SS2016 18-35) ) 85‐90 2016年10月

Unsupervised Bug Report Categorization Using Clustering and Labeling Algorithm

Nachai Limsettho, Hideaki Hata, Akito Monden, Kenichi Matsumoto

INTERNATIONAL JOURNAL OF SOFTWARE ENGINEERING AND KNOWLEDGE ENGINEERING 26 ( 7 ) 1027 - 1053 2016年9月

記述言語：英語出版者・発行元：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

Web of Science

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Investigating and Projecting Population Structures in Open Source Software Projects: A Case Study of Projects in GitHub

Saya Onoue, Hideaki Hata, Akito Monden, Kenichi Matsumoto

IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS E99D ( 5 ) 1304 - 1315 2016年5月

記述言語：英語出版者・発行元：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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LSA-X: Exploiting Productivity Factors in Linear Size Adaptation for Analogy-Based Software Effort Estimation

Passakorn Phannachitta, Akito Monden, Jacky Keung, Kenichi Matsumoto

IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS E99D ( 1 ) 151 - 162 2016年1月

記述言語：英語出版者・発行元：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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Filter-INC: Handling Effort-Inconsistency in Software Effort Estimation Datasets

Passakorn Phannachitta, Jacky Keung, Kwabena Ebo Bennin, Akito Monden, Kenichi Matsumoto

2016 23RD ASIA-PACIFIC SOFTWARE ENGINEERING CONFERENCE (APSEC 2016) 185 - 192 2016年

記述言語：英語出版者・発行元：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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Task purpose estimation in software development based on automatic measurement data and machine learning

Ohashi, R., Matsumoto, K., Uwano, H., Monden, A., Araki, K., Yamada, K.

Computer Software 33 ( 2 ) 139 - 150 2016年

自動計測データと機械学習に基づくソフトウェア開発の作業目的の推定

大橋亮太, 上野秀剛, 門田暁人, 荒木健史, 山田欣吾, 松本健一

コンピュータソフトウェア 33 ( 2 ) 139 - 150 2016年

Measuring difficulty of program comprehension based on brain activation

Takao Nakagawa, Ysutaka Kamei, Hidetake Uwano, Akito Monden, Naoyasu Ubayashi, Kenichi Matsumoto

Computer Software 33 ( 2 ) 78 - 89 2016年

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

Scopus

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脳活動に基づくプログラム理解の困難さ測定

中川尊雄, 亀井靖高, 上野秀剛, 門田暁人, 鵜林尚靖, 松本健一

コンピュータソフトウェア 33 ( 2 ) 78 - 89 2016年

Investigating the Effects of Balanced Training and Testing Datasets on Effort-Aware Fault Prediction Models

Kwabena Ebo Bennin, Jacky Keung, Akito Monden, Yasutaka Kamei, Naoyasu Ubayashi

PROCEEDINGS 2016 IEEE 40TH ANNUAL COMPUTER SOFTWARE AND APPLICATIONS CONFERENCE WORKSHOPS, VOL 1 154 - 163 2016年

記述言語：英語出版者・発行元：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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Identifying Recurring Association Rules in Software Defect Prediction

Takashi Watanabe, Akito Monden, Yasutaka Kamei, Shuji Morisaki

2016 IEEE/ACIS 15TH INTERNATIONAL CONFERENCE ON COMPUTER AND INFORMATION SCIENCE (ICIS) 861 - 866 2016年

記述言語：英語出版者・発行元：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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A Fuzzy Hashing Technique for Large Scale Software Birthmarks

Takehiro Tsuzaki, Teruaki Yamamoto, Haruaki Tamada, Akito Monden

2016 IEEE/ACIS 15TH INTERNATIONAL CONFERENCE ON COMPUTER AND INFORMATION SCIENCE (ICIS) 867 - 872 2016年

記述言語：英語出版者・発行元：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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Analysis of Information System Operation Cost Based on Working Time and Unit Cost

Masateru Tsunoda, Kenichi Matsumoto, Akito Monden, Sawako Ohiwa, Tomoki Oshino

2016 IEEE/ACIS 15TH INTERNATIONAL CONFERENCE ON COMPUTER AND INFORMATION SCIENCE (ICIS) 837 - 842 2016年

記述言語：英語出版者・発行元：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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Influence of Outliers on Analogy Based Software Development Effort Estimation

Kenichi Ono, Akito Monden, Masateru Tsunoda, Kenichi Matsumoto

2016 IEEE/ACIS 15TH INTERNATIONAL CONFERENCE ON COMPUTER AND INFORMATION SCIENCE (ICIS) 849 - 854 2016年

記述言語：英語出版者・発行元：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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ソフトウェアテストにおける静的解析ツールの段階的適用による不具合修正工数の更なる低減 ―民生用音響・映像機器向け組み込みソフトウェア開発へのQACの段階的適用とその実証評価―

鶴田雅明, 大平雅雄, 門田暁人, 松本健一

情報社会学会誌 11 ( 1 ) 5 - 16 2016年

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