Faculty Profiles - MONDEN Akito

Papers - MONDEN Akito

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Task estimation for software company employees based on computer interaction logs Reviewed

Florian Pellegrin, Zeynep Yücel, Akito Monden, Pattara Leelaprute

Empir. Softw. Eng. 26 ( 5 ) 98 - 98 2021.9

On the relative value of data resampling approaches for software defect prediction Reviewed

Kwabena Ebo Bennin, Jacky W. Keung, Akito Monden

Empirical Software Engineering 24 ( 2 ) 602 - 636 2019.4

Authorship：Corresponding author Language：English Publishing type：Research paper (scientific journal) Publisher：Springer New York LLC

Software defect data sets are typically characterized by an unbalanced class distribution where the defective modules are fewer than the non-defective modules. Prediction performances of defect prediction models are detrimentally affected by the skewed distribution of the faulty minority modules in the data set since most algorithms assume both classes in the data set to be equally balanced. Resampling approaches address this concern by modifying the class distribution to balance the minority and majority class distribution. However, very little is known about the best distribution for attaining high performance especially in a more practical scenario. There are still inconclusive results pertaining to the suitable ratio of defect and clean instances (Pfp), the statistical and practical impacts of resampling approaches on prediction performance and the more stable resampling approach across several performance measures. To assess the impact of resampling approaches, we investigated the bias and effect of commonly used resampling approaches on prediction accuracy in software defect prediction. Analyzes of six resampling approaches on 40 releases of 20 open-source projects across five performance measures and five imbalance rates were performed. The experimental results obtained indicate that there were statistical differences between the prediction results with and without resampling methods when evaluated with the geometric-mean, recall(pd), probability of false alarms(pf ) and balance performance measures. However, resampling methods could not improve the AUC values across all prediction models implying that resampling methods can help in defect classification but not defect prioritization. A stable Pfp rate was dependent on the performance measure used. Lower Pfp rates are required for lower pf values while higher Pfp values are required for higher pd values. Random Under-Sampling and Borderline-SMOTE proved to be the more stable resampling method across several performance measures among the studied resampling methods. Performance of resampling methods are dependent on the imbalance ratio, evaluation measure and to some extent the prediction model. Newer oversampling methods should aim at generating relevant and informative data samples and not just increasing the minority samples.

DOI： 10.1007/s10664-018-9633-6

Scopus

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MAHAKIL: Diversity Based Oversampling Approach to Alleviate the Class Imbalance Issue in Software Defect Prediction Reviewed

Bennin, K.E., Keung, J., Phannachitta, P., Monden, A., Mensah, S.

IEEE Transactions on Software Engineering 44 ( 6 ) 534 - 550 2018.6

Extended Association Rule Mining and Its Application to Software Engineering Data Sets Reviewed

Hidekazu Saito, Kinari Nishiura, Akito Monden, Shuji Morisaki

International Journal of Software Engineering and Knowledge Engineering 34 ( 11 ) 1735 - 1756 2024.11

Authorship：Corresponding author Language：English Publishing type：Research paper (scientific journal) Publisher：World Scientific Pub Co Pte Ltd

Association rule mining is a highly effective approach to data analysis for datasets of varying sizes, accommodating diverse feature values. Nevertheless, deriving practical rules from datasets with numerical variables presents a challenge, as these variables must be discretized beforehand. Quantitative association rule mining addresses this issue, allowing the extraction of valuable rules. This paper introduces an extension to quantitative association rules, incorporating a two-variable function in their consequent part. The use of correlation functions, statistical test functions, and error functions is also introduced. We illustrate the utility of this extension through three case studies employing software engineering datasets. In case study 1, we successfully pinpointed the conditions that result in either a high or low correlation between effort and software size, offering valuable insights for software project managers. In case study 2, we effectively identified the conditions that lead to a high or low correlation between the number of bugs and source lines of code, aiding in the formulation of software test planning strategies. In case study 3, we applied our approach to the two-step software effort estimation process, uncovering the conditions most likely to yield low effort estimation errors.

DOI： 10.1142/s0218194024500347

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On Applying Bandit Algorithm to Fault Localization Techniques Reviewed

Masato Nakao, Kensei Hamamoto, Masateru Tsunoda, Amjed Tahir, Koji Toda, Akito Monden, Keitaro Nakasai, Kenichi Matsumoto

2024 IEEE 35th International Symposium on Software Reliability Engineering Workshops (ISSREW) 111 - 112 2024.10

Demystifying Ambiguous Words in Request for Proposals of Information Systems in Japan Reviewed

Toru Nakamichi, Hiroki Inayoshi, Kinari Nishiura, Mariko Sasakura, Akito Monden

International Journal of Software Innovation 12 ( 1 ) 1 - 18 2024.10

Authorship：Last author,　Corresponding author Language：English Publishing type：Research paper (scientific journal) Publisher：IGI Global

<p>Disagreements in interpreting words in software requirements specifications (SRSs) can lead to project failure. Various approaches to identifying and preventing ambiguous words in SRSs have been proposed. Yet, it is unclear which ambiguous words are used in the actual SRSs and to what extent they need to be modified. This paper quantitatively analyzes existing SRSs to clarify (1) how many ambiguous words are included in SRSs and (2) how many of these words require correction. This paper targets the Request for Proposals (RFPs), which describe the initial requirements of 40 systems of local governments, libraries, universities, and hospitals in Japan. Ten ambiguous Japanese words were analyzed. The result shows that “juubun” (sufficient) appeared most frequently, and 42% required correction when this word was used. The result also indicates that the number of ambiguous words varied greatly among the RFPs and that larger RFPs did not necessarily contain more ambiguous words.</p>

DOI： 10.4018/ijsi.358012

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過去と現在進行中のテスト結果を組み合わせたソフトウェアバグ予測 Reviewed

廣瀬椋一, 西浦生成, 角田雅照, 門田暁人

コンピュータソフトウェア 41 ( 1 ) 2024.10

Comparison of Similarity Functions for n-gram Software Birthmarks Reviewed

Nikolay Fedorov, Haruaki Tamada, Hiroki Inayoshi, Akito Monden

Proc. 6th World Symposium on Software Engineering (WSSE2024) 2024.9

Analyzing Class Usage in JavaScript Programs Reviewed

Kinari Nishiura, Shuto Misawa, Akito Monden

Proc. 6th World Symposium on Software Engineering (WSSE2024) 2024.9

An Empirical Study on Ambiguous Words in Software Requirements Specifications of Local Government and Library Systems Reviewed

Toru Nakamichi, Kinari Nishiura, Mariko Sasakura, Akito Monden

2024 IEEE/ACIS 22nd International Conference on Software Engineering Research, Management and Applications (SERA) 206 - 211 2024.5

Identifying Security Bugs in Issue Reports: Comparison of BERT, N-gram IDF and ChatGPT Reviewed

Daiki Yokoyama, Kinari Nishiura, Akito Monden

2024 IEEE/ACIS 22nd International Conference on Software Engineering Research, Management and Applications (SERA) 328 - 333 2024.5

Porting a Python Application to the Web Using Django: A Case Study of an Archaeological Image Processing System Reviewed

Hikaru Tomita, Mariko Sasakura, Kinari Nishiura, Hiroki Inayoshi, Akito Monden

2024 IEEE/ACIS 22nd International Conference on Software Engineering Research, Management and Applications (SERA) 199 - 205 2024.5

スマホゲームにおける課金誘導方法の分析 Reviewed

麻生直希, 西浦生成, 笹倉万里子, 門田暁人

コンピュータソフトウェア 31 ( 1 ) 1_75 - 1_81 2024.1

Using a Personality-Aware Recommendation System for Comparing Inventory Performances Reviewed

Natsu Nishimura, Zeynep Yücel, Akito Monden

2023 15th International Congress on Advanced Applied Informatics Winter (IIAI-AAI-Winter) 228 - 235 2023.12

Dependence of Perception of Vocabulary Difficulty on Contexture Reviewed

Parisa Supitayakul, Rika Kuramitsu, Zeynep Yucel, Akito Monden, Koichi Takeuchi

2023 15th International Congress on Advanced Applied Informatics Winter (IIAI-AAI-Winter) 129 - 134 2023.12

Experiment Design and Verification for Assessing the Acquisition of Strategic Planning Ability Reviewed

Natchanon Manatphaiboon, Shogo Hamachi, Zeynep Yücel, Pattara Leelaprute, Akito Monden

2023 15th International Congress on Advanced Applied Informatics Winter (IIAI-AAI-Winter) 121 - 128 2023.12

Fault-proneモジュール予測における第三者データに基づいた外れ値除去 Reviewed

西浦生成, 門田暁人

コンピュータソフトウェア 40 ( 4 ) 2023.11

Software Defect Prediction by Online Learning Considering Defect Overlooking Reviewed

Yuta Yamasaki, Nikolay Fedorov, Masateru Tsunoda, Akito Monden, Amjed Tahir, Kwabena Ebo Bennin, Koji Toda, Keitaro Nakasai

2023 IEEE 34th International Symposium on Software Reliability Engineering Workshops (ISSREW) 2023.10

Exploring Behavioral Trustworthiness of GitHub Developers Reviewed

Kinari Nishiura, Kaito Ikeda, Mariko Sasakura, Akito Monden

Proc. 5th World Symposium on Software Engineering (WSSE2023) 2023.9

Subject Experiments with a Learning Support System for Grover's Algorithm Reviewed

Hayato Yasunaga, Mariko Sasakura, Akito Monden

Proc. 27th International Conference Information Visualisation (IV2023) 125 - 130 2023.7

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