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An Empirical Study on Refactoring-Inducing Pull Requests

Published: 11 October 2021 Publication History

Abstract

Background: Pull-based development has shaped the practice of Modern Code Review (MCR), in which reviewers can contribute code improvements, such as refactorings, through comments and commits in Pull Requests (PRs). Past MCR studies uniformly treat all PRs, regardless of whether they induce refactoring or not. We define a PR as refactoring-inducing, when refactoring edits are performed after the initial commit(s), as either a result of discussion among reviewers or spontaneous actions carried out by the PR developer. Aims: This mixed study (quantitative and qualitative) explores code reviewing-related aspects intending to characterize refactoring-inducing PRs. Method: We hypothesize that refactoring-inducing PRs have distinct characteristics than non-refactoring-inducing ones and thus deserve special attention and treatment from researchers, practitioners, and tool builders. To investigate our hypothesis, we mined a sample of 1,845 Apache's merged PRs from GitHub, mined refactoring edits in these PRs, and ran a comparative study between refactoring-inducing and non-refactoring-inducing PRs. We also manually examined 2,096 review comments and 1,891 detected refactorings from 228 refactoring-inducing PRs. Results: We found 30.2% of refactoring-inducing PRs in our sample and that they significantly differ from non-refactoring-inducing ones in terms of number of commits, code churn, number of file changes, number of review comments, length of discussion, and time to merge. However, we found no statistical evidence that the number of reviewers is related to refactoring-inducement. Our qualitative analysis revealed that at least one refactoring edit was induced by review in 133 (58.3%) of the refactoring-inducing PRs examined. Conclusions: Our findings suggest directions for researchers, practitioners, and tool builders to improve practices around pull-based code review.

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cover image ACM Conferences
ESEM '21: Proceedings of the 15th ACM / IEEE International Symposium on Empirical Software Engineering and Measurement (ESEM)
October 2021
368 pages
ISBN:9781450386654
DOI:10.1145/3475716
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  1. code review mining
  2. empirical study
  3. refactoring-inducing pull request

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