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Bug datasets consisting of real-world bugs are important artifacts forresearchers and programmers, which lay empirical and experimental foundationfor various SE/PL research such as fault localization, software testing, andprogram repair. All known state-of-the-art datasets are constructed manually,which inevitably limits their scalability, representativeness, and the supportfor the emerging data-driven research. In this work, we propose an approach toautomate the process of harvesting replicable regression bugs from the codeevolutionary history. We focus on regression bug dataset, as they (1) manifesthow a bug is introduced and fixed (as normal bugs), (2) support regression buganalysis, and (3) incorporate a much stronger specification (i.e., the originalpassing version) for general bug analysis. Technically, we address aninformation retrieval problem on code evolution history. Given a coderepository, we search for regressions where a test can pass a regression-fixingcommit, fail a regressioninducing commit, and pass a working commit. In thiswork, we address the challenges of (1) identifying potential regression-fixingcommits from the code evolution history, (2) migrating the test and its codedependencies over the history, and (3) minimizing the compilation overheadduring the regression search. We build our tool, RegMiner, which harvested 537regressions over 66 projects for 3 weeks, created the largest replicableregression dataset within shortest period, to the best of our knowledge.Moreover, our empirical study on our regression dataset shows a gap between thepopular regression fault localization techniques (e.g, delta-debugging) and thereal fix, revealing new data-driven research opportunities.

RegMiner: Towards Constructing a Large Regression Dataset from Code Evolution History