On the Impact of Draft Pull Requests on Accelerating Feedback
Bibliographic record
Abstract
The pull request (PR) mechanism provides a structured way for developers to present their proposed modifications, engage in code review, and address any concerns before the changes are incorporated into the main codebase. As the adoption of PRs has grown over time, a variant known as draft PRs has gained traction, offering developers a novel way to share ideas and get feedback and collaboration on code changes during their development. Despite the benefits that draft PRs offer, there is still a lack of research on their efficiency in reaching the goal for which they are conceived, which is getting early feedback. This research paper aims to fill this gap by exploring how the draft mechanism is used, the impact of the draft mechanism on getting feedback and on the integration of new changes, and the different factors contributing to the responsiveness of comments. We observe that the draft is used by practitioners for complex changes and for different purposes, including the discussion of features to implement, experiments to conduct, and new versions to release. However, the goal of the draft mechanism is not fully reached as practitioners do not receive feedback (i.e., issue or review comments) on their drafts. For that, we leverage explanatory machine learning models to understand the differences between drafts that receive comments from these that did not before becoming ready to review. Our models show a median AUC performance of 0.67 to 0.77 and 0.66 to 0.75 for the issue comments and review comments models, respectively. The interpretation of our models shows that the actions that developers take as events, the description of the draft, the engagement of the author of the draft, and the engagement and responsiveness of the main reviewers play a crucial role in encouraging the reception of comments for draft pull requests.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.085 | 0.460 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".