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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Bibliographic Details
Main Authors: Rastogi, A. (Author), Wang, H. (Author), Wang, T. (Author), Yu, Y. (Author), Zhang, X. (Author)
Format: Article
Language:English
Published: Springer 2022
Subjects:
Online Access:View Fulltext in Publisher
LEADER 02908nam a2200337Ia 4500
001 10.1007-s10664-022-10143-4
008 220718s2022 CNT 000 0 und d
020 |a 13823256 (ISSN) 
245 1 0 |a Pull request latency explained: an empirical overview 
260 0 |b Springer  |c 2022 
856 |z View Fulltext in Publisher  |u https://doi.org/10.1007/s10664-022-10143-4 
520 3 |a 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. © 2022, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature. 
650 0 4 |a Development scenarios 
650 0 4 |a Distributed software development 
650 0 4 |a Github 
650 0 4 |a GitHub 
650 0 4 |a Literature reviews 
650 0 4 |a Processing time 
650 0 4 |a Pull request latency 
650 0 4 |a Pull-based development 
650 0 4 |a Regression analysis 
650 0 4 |a Related works 
650 0 4 |a Review process 
650 0 4 |a Software design 
650 0 4 |a Speed up 
700 1 |a Rastogi, A.  |e author 
700 1 |a Wang, H.  |e author 
700 1 |a Wang, T.  |e author 
700 1 |a Yu, Y.  |e author 
700 1 |a Zhang, X.  |e author 
773 |t Empirical Software Engineering