| 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 | 113 | 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 | 53 | 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 | 46 | 2020 |
| 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 | 18 | 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 | 16 | 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 | 5 | 2023 |
| 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 | 5 | 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 | 5 | 2022 |
| 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 | 4 | 2022 |
| 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 | 4 | 2020 |
| 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 | 3 | 2019 |
| 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 |
| 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 |
| 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, ... arXiv preprint arXiv:2402.01438, 2024 | 2 | 2024 |
| 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 | 2 | 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 |
| 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 |
| 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 |
| Challenges and future research direction for microtask programming in industry M Kondo, S Saito, Y Iimura, E Choi, O Mizuno, Y Kamei, N Ubayashi Proceedings of the 19th International Conference on Mining Software …, 2022 | 1 | 2022 |
| 類似した開発者の分類と不具合予測におけるその効果 北村紗也加， 近藤将成， 水野修 ソフトウェアエンジニアリングシンポジウム 2019 論文集 2019, 180-189, 2019 | 1 | 2019 |
Yasutaka KameiProfessor, Kyushu University, InaRIS FellowVerified email at ait.kyushu-u.ac.jp
