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Commit-Based Class-Level Defect Prediction for Python Projects

Khine Yin MON, Masanari KONDO, Eunjong CHOI, Osamu MIZUNO

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Summary :

Defect prediction approaches have been greatly contributing to software quality assurance activities such as code review or unit testing. Just-in-time defect prediction approaches are developed to predict whether a commit is a defect-inducing commit or not. Prior research has shown that commit-level prediction is not enough in terms of effort, and a defective commit may contain both defective and non-defective files. As the defect prediction community is promoting fine-grained granularity prediction approaches, we propose our novel class-level prediction, which is finer-grained than the file-level prediction, based on the files of the commits in this research. We designed our model for Python projects and tested it with ten open-source Python projects. We performed our experiment with two settings: setting with product metrics only and setting with product metrics plus commit information. Our investigation was conducted with three different classifiers and two validation strategies. We found that our model developed by random forest classifier performs the best, and commit information contributes significantly to the product metrics in 10-fold cross-validation. We also created a commit-based file-level prediction for the Python files which do not have the classes. The file-level model also showed a similar condition as the class-level model. However, the results showed a massive deviation in time-series validation for both levels and the challenge of predicting Python classes and files in a realistic scenario.

Publication
IEICE TRANSACTIONS on Information Vol.E106-D No.2 pp.157-165
Publication Date
2023/02/01
Publicized
2022/11/14
Online ISSN
1745-1361
DOI
10.1587/transinf.2022MPP0003
Type of Manuscript
Special Section PAPER (Special Section on Empirical Software Engineering)
Category

Authors

Khine Yin MON
  Kyoto Institute of Technology
Masanari KONDO
  Kyushu University
Eunjong CHOI
  Kyoto Institute of Technology
Osamu MIZUNO
  Kyoto Institute of Technology

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