SZZ in the time of Pull Requests

Fernando Petrulio; David Ackermann; Enrico Fregnan; Gül Calikli; Marco Castelluccio; Sylvestre Ledru; Calixte Denizet; Emma Humphries; Alberto Bacchelli

arXiv · 2209.03311

SZZ in the time of Pull Requests

Fernando Petrulio·David Ackermann·Enrico Fregnan·Gül Calikli·Marco Castelluccio·Sylvestre Ledru·Calixte Denizet·Emma Humphries·Alberto Bacchelli

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Abstract

In the multi-commit development model, programmers complete tasks (e.g., implementing a feature) by organizing their work in several commits and packaging them into a commit-set. Analyzing data from developers using this model can be useful to tackle challenging developers' needs, such as knowing which features introduce a bug as well as assessing the risk of integrating certain features in a release. However, to do so one first needs to identify fix-inducing commit-sets. For such an identification, the SZZ algorithm is the most natural candidate, but its performance has not been evaluated in the multi-commit context yet. In this study, we conduct an in-depth investigation on the reliability and performance of SZZ in the multi-commit model. To obtain a reliable ground truth, we consider an already existing SZZ dataset and adapt it to the multi-commit context. Moreover, we devise a second dataset that is more extensive and directly created by developers as well as Quality Assurance (QA) engineers of Mozilla. Based on these datasets, we (1) test the performance of B-SZZ and its non-language-specific SZZ variations in the context of the multi-commit model, (2) investigate the reasons behind their specific behavior, and (3) analyze the impact of non-relevant commits in a commit-set and automatically detect them before using SZZ.

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BibTeX RIS

Fernando Petrulio, David Ackermann, Enrico Fregnan, Gül Calikli, Marco Castelluccio, Sylvestre Ledru, Calixte Denizet, Emma Humphries, Alberto Bacchelli. 2022-09-07. SZZ in the time of Pull Requests. https://arxiv.org/abs/2209.03311

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