Dissection of a Bug Dataset: Anatomy of 395 Patches from Defects4J

by   Victor Sobreira, et al.
KTH Royal Institute of Technology

Well-designed and publicly available datasets of bugs are an invaluable asset to advance research fields such as fault localization and program repair. They allow directly and fairly comparison between competing techniques and also the replication of experiments. These datasets need to be deeply understood by researchers: the answer for questions like "which bugs can my technique handle?" and "for which bugs is my technique effective?" depends on the comprehension of properties related to bugs and their patches. However, such properties are usually not included in the datasets, and there is still no widely adopted methodology for characterizing bugs and patches. In this work, we deeply study 395 patches of the Defects4J dataset. Quantitative properties (patch size and spreading) were automatically extracted, whereas qualitative ones (repair actions and patterns) were manually extracted using a thematic analysis-based approach. We found that 1) the median size of Defects4J patches is four lines, and almost 30 92 the top-3 most applied repair actions are addition of method calls, conditionals, and assignments, occurring in 77 repair patterns were found for 95 appearing in 43 useful for researchers to perform advanced analysis on their techniques' results based on Defects4J. Moreover, our set of properties can be used to characterize and compare different bug datasets.


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Defects4J Dissection presents data to help researchers and practitioners to better understand the Defects4J bug dataset

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