An Empirical Study on Code Review Activity Prediction and Its Impact in Practice
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
During code reviews, an essential step in software quality assurance, reviewers have the difficult task of understanding and evaluating code changes to validate their quality and prevent introducing faults to the codebase. This is a tedious process where the effort needed is highly dependent on the code submitted, as well as the author’s and the reviewer’s experience, leading to median wait times for review feedback of 15-64 hours. Through an initial user study carried with 29 experts, we found that re-ordering the files changed by a patch within the review environment has potential to improve review quality, as more comments are written (+23%), and participants’ file-level hot-spot precision and recall increases to 53% (+13%) and 28% (+8%), respectively, compared to the alphanumeric ordering. Hence, this paper aims to help code reviewers by predicting which files in a submitted patch need to be (1) commented, (2) revised, or (3) are hot-spots (commented or revised). To predict these tasks, we evaluate two different types of text embeddings (i.e., Bag-of-Words and Large Language Models encoding) and review process features (i.e., code size-based and history-based features). Our empirical study on three open-source and two industrial datasets shows that combining the code embedding and review process features leads to better results than the state-of-the-art approach. For all tasks, F1-scores (median of 40-62%) are significantly better than the state-of-the-art (from +1 to +9%).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.072 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it