Mining Software Defects: Should We Consider Affected Releases?
With the rise of the Mining Software Repositories (MSR) field, defect datasets extracted from software repositories play a foundational role in many empirical studies related to software quality. At the core of defect data preparation is the identification of post-release defects. Prior studies leverage many heuristics (e.g., keywords and issue IDs) to identify post-release defects. However, such the heuristic approach is based on several assumptions, which pose common threats to the validity of many studies. In this paper, we set out to investigate the nature of the difference of defect datasets generated by the heuristic approach and the realistic approach that leverages the earliest affected release that is realistically estimated by a software development team for a given defect. In addition, we investigate the impact of defect identification approaches on the predictive accuracy and the ranking of defective modules that are produced by defect models. Through a case study of defect datasets of 32 releases, we find that the heuristic approach has a large impact on both defect count datasets and binary defect datasets. Surprisingly, we find that the heuristic approach has a minimal impact on defect count models, suggesting that future work should not be too concerned about defect count models that are constructed using heuristic defect datasets. On the other hand, using defect datasets generated by the realistic approach leads to an improvement in the predictive accuracy of defect classification models.
Fri 31 May Times are displayed in time zone: (GMT-04:00) Eastern Time (US & Canada) change
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Class Imbalance Evolution and Verification Latency in Just-in-Time Software Defect PredictionTechnical Track
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The Impact of Class Rebalancing Techniques on the Performance and Interpretation of Defect Prediction ModelsJournal-First
Chakkrit (Kla) TantithamthavornMonash University, Australia, Ahmed E. HassanQueen's University, Kenichi MatsumotoNara Institute of Science and TechnologyPre-print
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Cristina MonniUniversità della Svizzera Italiana, Mauro PezzeUniversità della Svizzera italiana (USI) (Switzerland) and Università degli Studi di Milano Bicocca (Italy)Pre-print
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