Investigating the Understandability of Review Comments on Code Change Requests
Bibliographic record
Abstract
Code review is a widely adopted quality assurance practice in software engineering, where expert reviewers assess developers’ code changes before merging. While prior studies have explored review comment quality and usefulness, they often overlook the clarity and understandability of Code Change Request (CCR) comments. Unclear CCR comments can pose significant challenges for developers to address. Therefore, this study investigates the prevalence and impact of confusing or unclear CCR comments and proposes two approaches to enhance CCR communication during code review. Using a dataset of 182 open-source GitHub projects with over 55 K pull requests and 466 K CCR comments, we analyzed how often unclear comments occur and their effects on the review process. Our classifier, built from manually annotated developers’ replies in response to CCR comments, revealed that $24 \%$ of comments led to author confusion. Statistical analysis shows that unclear CCR comments significantly increase resolution time and discussion length, and that pull requests with clear CCR comments are more likely to be addressed and merged. A manual analysis of 400 confusing CCR comments identified six key characteristics, with lack of clarity and unclear rationale being the most common. Our first approach, the confusion classifier, flags authors’ confusion to enable reviewers to clarify ambiguities promptly (recall of 0.96), while the second classifier enables reviewers to evaluate the clarity and understandability of their CCR comments (recall of 0.93). This pioneering study further provides recommendations for enhancing CCR comments and offering a foundation for future research to streamline the review process.
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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 | 0.078 | 0.562 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.012 | 0.006 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".