Leveraging test plan quality to improve code review efficacy
Bibliographic record
Abstract
In modern code reviews, many artifacts play roles in knowledge- sharing and documentation: summaries, test plans, and comments, etc. Improving developer tools and facilitating better code reviews require an understanding of the quality of pull requests and their artifacts. This is difficult to measure, however, because they are often free-form natural language and unstructured text data. In this paper, we focus on measuring the quality of test plans at Meta. Test plans are used as a communication mechanism between the author of a pull request and its reviewers, serving as walkthroughs to help confirm that the changed code is behaving as expected. We collected developer opinions on over 650 test plans from more than 500 Meta developers, then introduced a transformer-based model to leverage the success of natural language processing (NLP) tech- niques in the code review domain. In our study, we show that the learned model is able to capture the sentiment of developers and reflect a correlation of test plan quality with review engagement and reversions: compared to a decision tree model, our proposed transformer-based model achieves a 7% higher F1-score. Finally, we present a case study of how such a metric may be useful in experiments to inform improvements in developer tools and experiences.
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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 | 0.037 | 0.193 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".