The impact of feature reduction techniques on defect prediction models M Kondo, CP Bezemer, Y Kamei, AE Hassan, O Mizuno Empirical Software Engineering 24, 1925-1963, 2019 | 119 | 2019 |
Code cloning in smart contracts: a case study on verified contracts from the ethereum blockchain platform M Kondo, GA Oliva, ZM Jiang, AE Hassan, O Mizuno Empirical Software Engineering 25, 4617-4675, 2020 | 54 | 2020 |
The impact of context metrics on just-in-time defect prediction M Kondo, DM German, O Mizuno, EH Choi Empirical software engineering 25, 890-939, 2020 | 54 | 2020 |
An empirical study on self-admitted technical debt in modern code review Y Kashiwa, R Nishikawa, Y Kamei, M Kondo, E Shihab, R Sato, ... Information and Software Technology 146, 106855, 2022 | 19 | 2022 |
An empirical study of utilization of imperative modules in ansible S Kokuryo, M Kondo, O Mizuno 2020 IEEE 20Th international conference on software quality, reliability and …, 2020 | 19 | 2020 |
Which metrics should researchers use to collect repositories: an empirical study K Yamamoto, M Kondo, K Nishiura, O Mizuno 2020 IEEE 20th International Conference on Software Quality, Reliability and …, 2020 | 8 | 2020 |
Pafl: Probabilistic automaton-based fault localization for recurrent neural networks Y Ishimoto, M Kondo, N Ubayashi, Y Kamei Information and Software Technology 155, 107117, 2023 | 7 | 2023 |
An empirical study of issue-link algorithms: which issue-link algorithms should we use? M Kondo, Y Kashiwa, Y Kamei, O Mizuno Empirical Software Engineering 27 (6), 136, 2022 | 7 | 2022 |
Towards privacy preserving cross project defect prediction with federated learning H Yamamoto, D Wang, GK Rajbahadur, M Kondo, Y Kamei, N Ubayashi 2023 IEEE International Conference on Software Analysis, Evolution and …, 2023 | 6 | 2023 |
Do visual issue reports help developers fix bugs? a preliminary study of using videos and images to report issues on GitHub H Kuramoto, M Kondo, Y Kashiwa, Y Ishimoto, K Shindo, Y Kamei, ... Proceedings of the 30th IEEE/ACM International Conference on Program …, 2022 | 6 | 2022 |
An empirical study of source code detection using image classification J Hong, O Mizuno, M Kondo 2019 10th International Workshop on Empirical Software Engineering in …, 2019 | 4 | 2019 |
Exploring the Effect of Multiple Natural Languages on Code Suggestion Using GitHub Copilot K Koyanagi, D Wang, K Noguchi, M Kondo, A Serebrenik, Y Kamei, ... 2024 IEEE/ACM 21st International Conference on Mining Software Repositories …, 2024 | 3 | 2024 |
深層学習によるソースコードコミットからの不具合混入予測 近藤将成, 森啓太, 水野修, 崔銀惠 情報処理学会論文誌 59 (4), 1250-1261, 2018 | 3 | 2018 |
Just-in-time defect prediction applying deep learning to source code changes M KONDO, K MORI, O MIZUNO, EH CHOI 情報処理学会論文誌ジャーナル (Web) 59 (4), 1250-1261, 2018 | 3 | 2018 |
Causal-Effect Analysis using Bayesian LiNGAM Comparing with Correlation Analysis in Function Point Metrics and Effort M Kondo, O Mizuno, EH Choi International Journal of Mathematical, Engineering and Management Sciences …, 2018 | 3 | 2018 |
Analysis on causal-effect relationship in effort metrics using Bayesian LiNGAM M Kondo, O Mizuno 2016 IEEE International Symposium on Software Reliability Engineering …, 2016 | 2 | 2016 |
When conversations turn into work: a taxonomy of converted discussions and issues in GitHub D Wang, M Kondo, Y Kamei, RG Kula, N Ubayashi Empirical Software Engineering 28 (6), 138, 2023 | 1 | 2023 |
Commit-Based Class-Level Defect Prediction for Python Projects KY Mon, M Kondo, E Choi, O Mizuno IEICE TRANSACTIONS on Information and Systems 106 (2), 157-165, 2023 | 1 | 2023 |
大規模言語モデルを用いた初学者のためのデバッグ作業支援の初期評価 K UTSUNOMIYA, G AKIYAMA, M KONDO, Y KAMEI, N UBAYASHI 電子情報通信学会技術研究報告 (Web) 122 (432 (SS2022 47-73)), 19-24, 2023 | 1 | 2023 |
Hey APR! Integrate Our Fault Localization Skill: Toward Better Automated Program Repair K Yamate, M Kondo, Y Kashiwa, Y Kamei, N Ubayashi 2022 IEEE 46th Annual Computers, Software, and Applications Conference …, 2022 | 1 | 2022 |