Recommending Code Reviews Leveraging Code Changes with Structured Information Retrieval
Bibliographic record
Abstract
Review comments are one of the main building blocks of modern code reviews. Manually writing code review comments could be time-consuming and technically challenging. Recently, an information retrieval (IR) based approach has been proposed to automatically recommend relevant code review comments for method-level code changes. However, this technique overlooks the structured items (e.g., class name, library information) from the source code and is applicable only for method-level changes. In this paper, we propose a novel technique for relevant review comments recommendation – RevCom – that leverages various code-level changes using structured information retrieval. RevCom uses different structured items from source code and can recommend relevant reviews for all types of changes (e.g., method-level and non-method-level). Our evaluation using three performance metrics show that RevCom outperforms both IR-based and DL-based baselines by up to 49.45% and 23.57% margins in BLEU score in recommending review comments. We find that RevCom can recommend review comments with an average BLEU score of ≈ 26.63%. According to Google’s AutoML Translation documentation, such a BLEU score indicates that the review comments can capture the original intent of the reviewers. All these findings suggest that RevCom can recommend relevant code reviews and has the potential to reduce the cognitive effort of human code reviewers.
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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 | 0.004 | 0.033 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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".