Researchers working with software repositories, often when building performance or quality models, need to recover traceability links between bug reports in issue tracking repositories and reviews in code review systems. However, very often the information stored in bug tracking repositories is not explicitly tagged or linked to the issues reviewing them. Researchers have to adopt various heuristics to tag the data. These includes, for example, identifying if an issue is a bug report or not. In this study we promote a research artifact in software engineering, a reusable unit of research that can be used to support other research endeavors and has acted as a support material that enabled the creation of the results published in a great number of papers until now – linking issues and reviews of the code review software process. We present two state-of-the-practice algorithms on how to link issues and reviews, selecting as our case study the open source cloud computing project of OpenStack. OpenStack enforces strict development guidelines and rules on the quality of the data in its issue tracking repository. We empirically compare the outcome of the two approaches, highlighting the most prominent one.
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