Pull request latency explained: an empirical overview

Pull request latency evaluation is an essential application of effort evaluation in the pull-based development scenario. It can help the reviewers sort the pull request queue, remind developers about the review processing time, speed up the review process and accelerate software development. There i...

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Veröffentlicht in:Empirical software engineering : an international journal Jg. 27; H. 6
Hauptverfasser: Zhang, Xunhui, Yu, Yue, Wang, Tao, Rastogi, Ayushi, Wang, Huaimin
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York Springer US 01.11.2022
Springer Nature B.V
Schlagworte:
Compilers
Computer Science
Correlation
Datasets
Evaluation
Interpreters
Literature reviews
Programming Languages
Regression analysis
Regression models
Software development
Software engineering
Software Engineering/Programming and Operating Systems
Distributed software development
Pull request latency
GitHub
Pull-based development
ISSN:1382-3256, 1573-7616
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Zusammenfassung:Pull request latency evaluation is an essential application of effort evaluation in the pull-based development scenario. It can help the reviewers sort the pull request queue, remind developers about the review processing time, speed up the review process and accelerate software development. There is a lack of work that systematically organizes the factors that affect pull request latency. Also, there is no related work discussing the differences and variations in characteristics in different scenarios and contexts. In this paper, we collected relevant factors through a literature review approach. Then we assessed their relative importance in five scenarios and six different contexts using the mixed-effects linear regression model. The most important factors differ in different scenarios. The length of the description is most important when pull requests are submitted. The existence of comments is most important when closing pull requests, using CI tools, and when the contributor and the integrator are different. When there exist comments, the latency of the first comment is the most important. Meanwhile, the influence of factors may change in different contexts. For example, the number of commits in a pull request has a more significant impact on pull request latency when closing than submitting due to changes in contributions brought about by the review process. Both human and bot comments are positively correlated with pull request latency. In contrast, the bot’s first comments are more strongly correlated with latency, but the number of comments is less correlated. Future research and tool implementation needs to consider the impact of different contexts. Researchers can conduct related studies based on our publicly available datasets and replication scripts.
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ISSN:1382-3256
1573-7616
DOI:10.1007/s10664-022-10143-4