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With the advent of open source software, a veritable treasure trove ofpreviously proprietary software development data was made available. Thisopened the field of empirical software engineering research to anyone inacademia. Data that is mined from software projects, however, requiresextensive processing and needs to be handled with utmost care to ensure validconclusions. Since the software development practices and tools have changedover two decades, we aim to understand the state-of-the-art research workflowsand to highlight potential challenges. We employ a systematic literature reviewby sampling over one thousand papers from leading conferences and by analyzingthe 286 most relevant papers from the perspective of data workflows,methodologies, reproducibility, and tools. We found that an important part ofthe research workflow involving dataset selection was particularly problematic,which raises questions about the generality of the results in existingliterature. Furthermore, we found a considerable number of papers providelittle or no reproducibility instructions -- a substantial deficiency for adata-intensive field. In fact, 33% of papers provide no information on howtheir data was retrieved. Based on these findings, we propose ways to addressthese shortcomings via existing tools and also provide recommendations toimprove research workflows and the reproducibility of research.

How are Software Repositories Mined? A Systematic Literature Review of Workflows, Methodologies, Reproducibility, and Tools