研究者詳細 - 伊原　彰紀

論文 - 伊原　彰紀

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ソフトウェア開発記録の多次元データ分析に向けた可視化方式Treemap Forestの設計と実証的評価

中川尊雄, 伊原彰紀, 松本健一 (担当区分：責任著者 )

SEC Journal ( 情報処理推進機構技術本部ソフトウェア・エンジニアリング・センター ) 12 ( 1 ) 8 - 15 2016年 [査読有り]

『オープンソースソフトウェア工学』シリーズオープンソースソフトウェア工学

伊原彰紀, 大平雅雄 (担当区分：筆頭著者,　責任著者 )

コンピュータソフトウェア ( 日本ソフトウェア科学会 ) 33 ( 1 ) 1_28 - 1_40 2016年 [査読有り]

A Study of Redundant Metrics in Defect Prediction Datasets

Jirayus Jiarpakdee, Chakkrit Tantithamthavorn, Akinori Ihara, Kenichi Matsumoto (担当区分：責任著者 )

2016 IEEE 27TH INTERNATIONAL SYMPOSIUM ON SOFTWARE RELIABILITY ENGINEERING WORKSHOPS (ISSREW) ( IEEE ) 51 - 52 2016年 [査読有り]

Defect prediction models can help Software Quality Assurance (SQA) teams understand their past pitfalls that lead to defective modules. However, the conclusions that are derived from defect prediction models without mitigating redundant metrics issues may be misleading. In this paper, we set out to investigate if redundant metrics issues are affecting defect prediction studies, and its degree and causes of redundancy. Through a case study of 101 publicly-available defect datasets of systems that span both proprietary and open source domains, we observe that (1) 10%-67% of metrics of the studied defect datasets are redundant, and (2) the redundancy of metrics has to do with the aggregation functions of metrics. These findings suggest that researchers should be aware of redundant metrics prior to constructing a defect prediction model in order to maximize internal validity of their studies.

DOI

Code Review Participation: Game Theoretical Modeling of Reviewers in Gerrit Datasets

Norihito Kitagawa, Hideaki Hata, Akinori Ihara, Kiminao Kogiso, Kenichi Matsumoto

9TH IEEE/ACM INTERNATIONAL WORKSHOP ON COOPERATIVE AND HUMAN ASPECTS OF SOFTWARE ENGINEERING (CHASE 2016) ( IEEE ) 64 - 67 2016年 [査読有り]

Code review is a common practice for improving the quality of source code changes and expediting knowledge transfer in a development community. In modern code review, source code changes or patches are considered to be assessed and approved for integration by multiple reviews. However, from our empirical study, we found that some patches are reviewed by only one reviewer, and some reviewers did not continue the review discussion, which can have negative effects on software quality. To understand these reviewers' behaviors, we model the code review situation based on the snowdrift game, which is used to analyze social dilemmas. With this game-theoretical modeling, we found that it can explain reviewers' behaviors well.

DOI

Understanding Question Quality through Affective Aspect in Q&A Site

Jirayus Jiarpakdee, Akinori Ihara, Ken-ichi Matsumoto (担当区分：責任著者 )

2016 IEEE/ACM 1ST INTERNATIONAL WORKSHOP ON EMOTION AWARENESS IN SOFTWARE ENGINEERING (SEMOTION) ( IEEE ) 12 - 17 2016年 [査読有り]

Ever since the Internet has become widely available, question and answer sites have been used as a knowledge sharing service. Users ask the community about how to solve problems, hoping that there will be someone to provide a solution. However, not every question is answered. Eric Raymond claimed that how an user asks a question is important. Existing studies have presented ways to study the question quality by textual, community-based or affective features. In this paper, we investigated how affective features are related to the question quality, and we found that using affective features improves the prediction of question quality. Moreover, Favorite Vote Count feature has the highest influence on our prediction models.

DOI

The Impact of a Low Level of Agreement Among Reviewers in a Code Review Process

Toshiki Hirao, Akinori Ihara, Yuki Ueda, Passakorn Phannachitta, Ken-ichi Matsumoto (担当区分：責任著者 )

OPEN SOURCE SYSTEMS: INTEGRATING COMMUNITIES, OSS 2016 ( SPRINGER-VERLAG BERLIN ) 472 97 - 110 2016年 [査読有り]

Software code review systems are commonly used in software development. In these systems, many patches are submitted to improve the quality. To verify the quality, voting is commonly used by contributors; however, there still exists a major problem, namely, that reviewers do not always simply reach a broad agreement. In our previous study, we found that consensus is not usually reached, implying that an individual reviewer's final decision usually differs from that of the majority of the other reviewers. In this study, we further investigate the reasons why such situations often occur, and provide suggestions for better handling of these problems. Our analysis of the Qt and OpenStack project datasets allow us to suggest that a patch owner should select more appropriate reviewers who often agree with others' decisions.

DOI

The Impact of Mislabelling on the Performance and Interpretation of Defect Prediction Models

Chakkrit Tantithamthavorn, Shane McIntosh, Ahmed E. Hassan, Akinori Ihara, Kenichi Matsumoto (担当区分：責任著者 )

Proceedings of the IEEE/ACM 37th IEEE International Conference on Software Engineering 2015 ( IEEE ) 812 - 823 2015年 [査読有り]

The reliability of a prediction model depends on the quality of the data from which it was trained. Therefore, defect prediction models may be unreliable if they are trained using noisy data. Recent research suggests that randomly-injected noise that changes the classification (label) of software modules from defective to clean (and vice versa) can impact the performance of defect models. Yet, in reality, incorrectly labelled (i.e., mislabelled) issue reports are likely non-random. In this paper, we study whether mislabelling is random, and the impact that realistic mislabelling has on the performance and interpretation of defect models. Through a case study of 3,931 manually-curated issue reports from the Apache Jackrabbit and Lucene systems, we find that: (1) issue report mislabelling is not random; (2) precision is rarely impacted by mislabelled issue reports, suggesting that practitioners can rely on the accuracy of modules labelled as defective by models that are trained using noisy data; (3) however, models trained on noisy data typically achieve 56%-68% of the recall of models trained on clean data; and (4) only the metrics in top influence rank of our defect models are robust to the noise introduced by mislabelling, suggesting that the less influential metrics of models that are trained on noisy data should not be interpreted or used to make decisions.

DOI

OSSシステムとコミュニティの共進化の理解を目的としたデータマイニング手法

山谷陽亮, 大平雅雄, PHANNACHITTA Passakorn, 伊原彰紀

情報処理学会論文誌 ( 一般社団法人情報処理学会 ) 56 ( 1 ) 59 - 71 2015年 [査読有り]

A Dataset of High Impact Bugs: Manually-Classified Issue Reports

Masao Ohira, Yutaro Kashiwa, Yosuke Yamatani, Hayato Yoshiyuki, Yoshiya Maeda, Nachai Limsettho, Keisuke Fujino, Hideaki Hata, Akinori Ihara, Kenichi Matsumoto

Proceedings of the 12th Working Conference on Mining Software Repositories 2015 ( IEEE ) 2015-August 518 - 521 2015年 [査読有り]

The importance of supporting test and maintenance activities in software development has been increasing, since recent software systems have become large and complex. Although in the field of Mining Software Repositories (MSR) there are many promising approaches to predicting, localizing, and triaging bugs, most of them do not consider impacts of each bug on users and developers but rather treat all bugs with equal weighting, excepting a few studies on high impact bugs including security, performance, blocking, and so forth. To make MSR techniques more actionable and effective in practice, we need deeper understandings of high impact bugs. In this paper we introduced our dataset of high impact bugs which was created by manually reviewing four thousand issue reports in four open source projects (Ambari, Camel, Derby and Wicket).

DOI

Toward Monitoring Bugs-fixing Process after the Releases in Open Source Software

Keisuke Fujino, Akinori Ihara, Kiyoshi Honda, Hironori Washizaki, Kenichi Matsumoto (担当区分：責任著者 )

6th International Workshop on Empirical Software Engineering in Practice (IWESEP 2014) 2014年11月 [査読有り]

Adaptive Search Framework: Better Search Result for Community

Papon Yongpisanpop, Masao Ohira, Akinori Ihara, Kenichi Matsumoto

情報社会学会誌 8 ( 2 ) 29 - 43 2014年03月 [査読有り]

Impact Analysis of Granularity Levels on Feature Location Technique

Chakkrit Tantithamthavorn, Akinori Ihara, Hideaki Hata, Kenichi Matsumoto

Communications in Computer and Information Science ( Springer ) 432 CCIS 135 - 149 2014年 [査読有り]

Due to the increasing of software requirements and software features, modern software systems continue to grow in size and complexity. Locating source code entities that required to implement a feature in millions lines of code is labor and cost intensive for developers. To this end, several studies have proposed the use of Information Retrieval (IR) to rank source code entities based on their textual similarity to an issue report. The ranked source code entities could be at a class or function granularity level. Source code entities at the class-level are usually large in size and might contain a lot of functions that are not implemented for the feature. Hence, we conjecture that the class-level feature location technique requires more effort than function-level feature location technique. In this paper, we investigate the impact of granularity levels on a feature location technique. We also presented a new evaluation method using effort-based evaluation. The results indicated that function-level feature location technique outperforms class-level feature location technique. Moreover, function-level feature location technique also required 7 times less effort than class-level feature location technique to localize the first relevant source code entity. Therefore, we conclude that feature location technique at the function-level of program elements is effective in practice. © Springer-Verlag Berlin Heidelberg 2014.

DOI

Early Identification of Future Committers in Open Source Software Projects

Akinori Ihara, Yasutaka Kamei, Masao Ohira, Ahmed E. Hassan, Naoyasu Ubayashi, Ken-ichi Matsumoto

2014 14TH INTERNATIONAL CONFERENCE ON QUALITY SOFTWARE (QSIC 2014) ( IEEE COMPUTER SOC ) 47 - 56 2014年 [査読有り]

There exists two types of developers in Open Source Software (OSS) projects: 1) Committers who have permission to commit edited source code to the Version Control System (VCS), 2) Developers who contribute source code but cannot commit to the VCS directly. In order to develop and evolve high quality OSS, projects are always in search of new committers. OSS projects often promote strong developers to become committers. When existing committers find strong developers, they propose their promotion to a committer role. Delaying the committer-promotion might lead to strong developers departing from an OSS project and the project losing them. However early committer-promotion comes with its own slew of risks as well (e.g., the promotion of inexperienced developers). Hence, committer-promotion decisions are critical for the quality and successful evolution of OSS projects. In this paper, we examine the committer-promotion phenomena for two OSS projects (Eclipse and Firefox). We find that the amount of activities by future committers was higher than the amount of activities by developers who did not become committers). We also find that some developers are promoted to a committer role very rapidly (within a few month) while some of developers take over one year to become a committer. Finally, we develop a committer-identification model to assist OSS projects identifying future committers.

DOI

Industry Questions About Open Source Software in Business: Research Directions and Potential Answers

Akinori Ihara, Akito Monden, Ken-ichi Matsumoto (担当区分：筆頭著者,　責任著者 )

Proceedings of the 6th International Workshop on Empirical Software Engineering in Practice 2014 ( IEEE ) 55 - 59 2014年 [査読有り]

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

ソフトウェア品質の第三者評価における探索的データ解析ツールの利用とその効果:OSSデータを対象とした検証実験

大平雅雄, 伊原彰紀, 中野大輔, 松本健一

SEC Journal ( 情報処理推進機構 ) 10 ( 1 ) 2014年 [査読有り]

「ソフトウェア工学の実証的アプローチ」シリーズ第5回ソフトウェアリポジトリマイニング

門田暁人, 伊原彰紀, 松本健一

コンピュウータソフトウェア 30 ( 2 ) 52 - 65 2013年05月 [査読有り]

OSS開発における不具合割当てパターンに着目した不具合修正時間の予測

正木仁, 大平雅雄, 伊原彰紀, 松本健一

情報処理学会論文誌 ( 情報処理学会 ) 54 ( 2 ) 933 - 944 2013年02月 [査読有り]

大規模OSSプロジェクトには日々大量の不具合が報告されており，不具合修正時間（OSSプロジェクトに報告された不具合が解決するまでに要する時間）をより正確に見積もることが重要になってきている．OSSプロジェクト管理者は，次期バージョンのリリースまでにどの不具合を修正すべきかを判断しなければならないためである．しかしながら，不具合の修正範囲の大きさや問題の複雑さの違い，ボランティアを主体とする開発者のスキルセットの違いなどの要因によって，個々の不具合の修正時間を見積もることは容易ではない．そのため近年，OSS開発における不具合修正時間の予測に関する研究がさかんに行われている．本論文では，不具合割当てパターンを用いて不具合修正時間の予測モデルを構築する．不具合割当てパターンとは，不具合修正タスク割当て時の不具合報告者・管理者・修正担当者の3者の社会的関係を分類したものである．不具合割当てパターンの違いにより，修正作業にとりかかるまでの時間および修正作業自体に要する時間はそれぞれ大きく異なることが知られている．従来研究の多くは不具合情報（重要度や優先度など）に基づいて予測モデルを構築しているが，不具合管理パターンを考慮することでさらなる予測精度の向上を期待できる．本論文では，Eclipse PlatformおよびJDTを対象として構築した予測モデルの評価を行った．実験の結果，不具合割当てパターンが不具合修正時間の予測精度向上に寄与するとともに，指定期間内（1週間以内など）に不具合修正が完了するか否かの判断を支援できることが分かった．さらに，本予測モデルを利用することで，Platformで約16%，JDTで約10%多くの不具合を修正可能と判断できることが分かった．The number of reported bugs has been increasing especially in large-scale open source software (OSS) projects. Project managers in the projects have to decide which bugs should be resolved until they release the next version of their products. However it is not easy to estimate the time to resolve each bug due to the differences of the size of required modifications, the difficulty of each modification, skill set of each developer, and so forth. To address this issue, many studies have tried to predict the bug fixing time in OSS development. In this paper we constructs a prediction model for the bug fixing time, using the bug assignment patterns which have an impact on developer's performance of fixing bugs. The bug assignment patterns categorize the social relationships among bug reporters, managers, and developers in assigning bug fixing tasks. While most studies in the past only used the information extracted from bug reports itself, taking the bug assignment patterns into account would lead to prediction results with higher accuracy. Using data from the Eclipse Platform and JDT projects, we evaluates the prediction model. As a result, we found that the bug assignment patterns improved the prediction accuracy and help OSS managers make sure if a target bug will be resolved in a specified period (e.g., one week). We also found that our prediction model can contribute to resolve more bugs until the specified release (about 16% in Platform and about 10% in JDT).

Studying re-opened bugs in open source software.

Emad Shihab, Akinori Ihara, Yasutaka Kamei, Walid M. Ibrahim, Masao Ohira, Bram Adams, Ahmed E. Hassan, Ken-ichi Matsumoto

Empirical Software Engineering 18 ( 5 ) 1005 - 1042 2013年 [査読有り]

DOI

『ソフトウェア工学の実証的アプローチ』シリーズ第5回ソフトウェアリポジトリマイニング

門田暁人, 伊原彰紀, 松本健一

コンピュータソフトウェア ( 日本ソフトウェア科学会 ) 30 ( 2 ) 52 - 65 2013年 [査読有り]

DOI

Using Co-Change Histories to Improve Bug Localization Performance

Chakkrit Tantithamthavorn, Akinori Ihara, Ken-ichi Matsumoto

2013 14TH ACIS INTERNATIONAL CONFERENCE ON SOFTWARE ENGINEERING, ARTIFICIAL INTELLIGENCE, NETWORKING AND PARALLEL/DISTRIBUTED COMPUTING (SNPD 2013) ( IEEE ) 543 - 548 2013年 [査読有り]

A large open source software (OSS) project receives many bug reports on a daily basis. Bug localization techniques automatically pinpoint source code fragments that are relevant to a bug report, thus enabling faster correction. Even though many bug localization methods have been introduced, their performance is still not efficient. In this research, we improved on existing bug localization methods by taking into account co-change histories. We conducted experiments on two OSS datasets, the Eclipse SWT 3.1 project and the Android ZXing project. We validated our approach by evaluating effectiveness compared to the state-of-the-art approach BugLocator. In the Eclipse SWT 3.1 project, our approach reliably identified source code that should be fixed for a bug in 72.46% of the total bugs, while BugLocator identified only 51.02%. In the Android ZXing project, our approach identified 85.71%, while BugLocator identified 60%.

DOI

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