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Data originating from open-source software projects provide valuableinformation to enhance software quality. In the scope of Software DefectPrediction, one of the most challenging parts is extracting valid data aboutfailure-prone software components from these repositories, which can helpdevelop more robust software. In particular, collecting data, calculatingmetrics, and synthesizing results from these repositories is a tedious anderror-prone task, which often requires understanding the programming languagesinvolved in the mined repositories, eventually leading to a proliferation oflanguage-specific data-mining software. This paper presents RepoMiner, alanguage-agnostic tool developed to support software engineering researchers increating datasets to support any study on defect prediction. RepoMinerautomatically collects failure data from software components, labels them asfailure-prone or neutral, and calculates metrics to be used as ground truth fordefect prediction models. We present its implementation and provide examples ofits application.

RepoMiner: a Language-agnostic Python Framework to Mine Software Repositories for Defect Prediction