Automatic patch linkage detection in code review using textual content and file location features

Context: Contemporary code review tools are a popular choice for software quality assurance. Using these tools, reviewers are able to post a linkage between two patches during a review discussion. Large development teams that use a review-then-commit model risk being unaware of these linkages. Objective: Our objective is to first explore how patch linkage impacts the review process. We then propose and evaluate models that detect patch linkage based on realistic time intervals. Method: First, we carry out an exploratory study on three open source projects to conduct linkage impact analysis using 942 manually classified linkages. Second, we propose two techniques using textual and file location similarity to build detection models and evaluate their performance. Results: The study provides evidence of latency in the linkage notification. We show that a patch with the Alternative Solution linkage (i.e., patches that implement similar functionality) undergoes a quicker review and avoids additional revisions after the team has been notified, compared to other linkage types. Our detection model experiments show promising recall rates for the Alternative Solution linkage (from 32% to 95%), but precision has room for improvement. Conclusion: Patch linkage detection is promising, with likely improvements if the practice of posting linkages becomes more prevalent. From our implications, this paper lays the groundwork for future research on how to increase patch linkage awareness to facilitate efficient reviews.

Automated summary

Contemporary code review tools are popular for software quality assurance, with the ability to post linkages between patches during discussions. Patch linkage notifications exhibit latency, with patches having Alternative Solution linkages undergoing quicker reviews and fewer revisions. Detection models show promising recall rates for Alternative Solution linkages, but precision can be improved.