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Understanding the Time to First Response In GitHub Pull Requests

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arxiv 2304.08426 v1 pith:K5FWYGW6 submitted 2023-04-17 cs.SE

classification cs.SE
keywords firstresponsedelaysdevelopmenthuman-generatedprojectsresponsesbots
verification ladder T0 review T1 audit T2 compute T3 formal
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The pull-based development is widely adopted in modern open-source software (OSS) projects, where developers propose changes to the codebase by submitting a pull request (PR). However, due to many reasons, PRs in OSS projects frequently experience delays across their lifespan, including prolonged waiting times for the first response. Such delays may significantly impact the efficiency and productivity of the development process, as well as the retention of new contributors as long-term contributors. In this paper, we conduct an exploratory study on the time-to-first-response for PRs by analyzing 111,094 closed PRs from ten popular OSS projects on GitHub. We find that bots frequently generate the first response in a PR, and significant differences exist in the timing of bot-generated versus human-generated first responses. We then perform an empirical study to examine the characteristics of bot- and human-generated first responses, including their relationship with the PR's lifetime. Our results suggest that the presence of bots is an important factor contributing to the time-to-first-response in the pull-based development paradigm, and hence should be separately analyzed from human responses. We also report the characteristics of PRs that are more likely to experience long waiting for the first human-generated response. Our findings have practical implications for newcomers to understand the factors contributing to delays in their PRs.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. On The Impact of Merge Request Deviations on Code Review Practices

    cs.SE 2025-06 conditional novelty 6.0 of 10

    A taxonomy of seven non-review merge request types, a few-shot classifier that detects them, and evidence that excluding them changes code review analytics.

  2. An Empirical Study on the Amount of Changes Required for Merge Request Acceptance

    cs.SE 2025-07 conditional novelty 5.0 of 10

    Up to 73 percent of GitLab merge requests are revised after submission, and a random forest model flags large-rework requests with AUC 0.84 to 0.88.

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