Faculty Profiles - MONDEN Akito

Papers - MONDEN Akito

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Human Resource Analysis Based on Used Libraries in Eclipse Projects on GitHub Reviewed

Wilson Chukwu Emmanuel, Akito Monden

Proc. 22nd IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing ( WP1-07 ) 1 - 4 2021.11

Effectiveness of Explaining a Program to Others in Finding Its Bugs Reviewed

Toshihiro Nakamura, Akito Monden, Mariko Sasakura, Hidetake Uwano

Proc. 22nd IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing ( WP1-06 ) 1 - 6 2021.11

Association Metrics Between Two Continuous Variables for Software Project Data Reviewed

Takumi Kanehira, Akito Monden, Zeynep Yucel

Proc. 22nd IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing ( WP1-05 ) 1 - 6 2021.11

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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Using Bandit Algorithms for Project Selection in Cross-Project Defect Prediction Reviewed

Takuya Asano, Masateru Tsunoda, Koji Toda, Amjed Tahir, Kwabena Ebo Bennin, Keitaro Nakasai, Akito Monden, Kenichi Matsumoto

2021 IEEE International Conference on Software Maintenance and Evolution (ICSME) 649 - 653 2021.9

ソフトウェア開発工数予測におけるデータスムージングの定量的評価 Reviewed

伊永健人, 門田暁人, Zeynep Yücel

コンピュータソフトウェア 38 ( 3 ) 3_83 - 3_89 2021.8

A Novel Approach to Address External Validity Issues in Fault Prediction Using Bandit Algorithms Reviewed

Teruki HAYAKAWA, Masateru TSUNODA, Koji TODA, Keitaro NAKASAI, Amjed TAHIR, Kwabena Ebo BENNIN, Akito MONDEN, Kenichi MATSUMOTO

IEICE Transactions on Information and Systems E104.D ( 2 ) 327 - 331 2021.2

Analysis of Work Efficiency and Quality of Software Maintenance Using Cross-Company Dataset Reviewed

Masateru TSUNODA, Akito MONDEN, Kenichi MATSUMOTO, Sawako OHIWA, Tomoki OSHINO

IEICE Transactions on Information and Systems E104.D ( 1 ) 76 - 90 2021.1

Influence of Outliers on Estimation Accuracy of Software Development Effort Reviewed

Kenichi Ono, Masateru Tsunoda, Akito Monden, Kenichi Matsumoto

IEICE Transactions on Information and Systems E104.D ( 1 ) 91 - 105 2021.1

Quantitative evaluation of data smoothing for software effort estimation

Kento Korenaga, Akito Monden, Zeynep Yücel

Computer Software 38 ( 3 ) 83 - 89 2021

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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Applying auto-sklearn to Software Development Effort Estimation.

Kazuya Tanaka, Akito Monden, Zeynep Yücel

Computer Software 38 ( 4 ) 46 - 52 2021

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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原型分析によるOSS開発者の貢献タイプの分析 Reviewed

池本和靖, 門田暁人

コンピュータソフトウェア 47 ( 4 ) 17 - 23 2020.11

ソフトウェア開発データを対象とする２変数間の尺度の検討

金平拓生, 門田暁人

情報処理学会第206回ソフトウェア工学研究会 2020-SE-206 ( 2 ) 2020.11

ランダムフォレストと相関ルールの組み合わせによるソフトウェアバグ予測の試行

瀬戸俊輝, 門田暁人

情報処理学会第206回ソフトウェア工学研究会 2020-SE-206 ( 3 ) 2020.11

ソフトウェアレビューのシミュレーションモデルとそのケーススタディ Reviewed

中原寛人, 門田暁人

ソフトウェア工学の基礎XXVII, 第27回ソフトウェア工学の基礎ワークショップFOSE2020 67 - 75 2020.11

ソフトウェア開発工数予測におけるAutoML の実験的評価 Reviewed

田中和也, 門田暁人

ソフトウェア工学の基礎XXVII, 第27回ソフトウェア工学の基礎ワークショップFOSE2020 141 - 142 2020.11

データ断片からのソフトウェア開発データの復元の試み Reviewed

西脇将樹, 門田暁人

ソフトウェア工学の基礎XXVII;回ソフトウェア工学の基礎ワークショップFOS 139 - 140 2020.11

バグを含む機械学習システムの挙動の分析の試行 Reviewed

木内祐太, 門田暁人

ソフトウェア工学の基礎XXVII, 第27回ソフトウェア工学の基礎ワークショップFOSE2020 147 - 148 2020.11

ソフトウェアバグ予測における学習データ切り替えの効果 Reviewed

廣瀬椋一, 門田暁人

ソフトウェア工学の基礎XXVII, 第27回ソフトウェア工学の基礎ワークショップFOSE2020 153 - 154 2020.11

ソフトウェア開発工数予測におけるデータスムージングの実験的評価 Reviewed

伊永健人, 門田暁人

ソフトウェア工学の基礎XXVII, 第27回ソフトウェア工学の基礎ワークショップFOSE2020 133 - 134 2020.11

Report on the 26th foundation of software engineering workshop (fose2019)

Shuji Morisaki, Masao Ohira, Akito Monden

Computer Software 37 ( 4 ) 3 - 9 2020.10

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