How Software Development Professionals Perceive the Use of Code Reviewer Recommendation Systems
Bibliographic record
Abstract
Selecting a code reviewer within a software development project is essential to the software development process. Code Reviewer Recommendation Systems (CRRSs) have been developed to reduce cognitive load and make decision-making faster and more efficient. This study provides a step toward understanding and improving the effectiveness of CRRSs by improving knowledge about their key aspects and the specific information needs of individuals involved in using them. We surveyed over 90 software project members to understand the important and missing features of CRRSs. We found that those who use AI for code recommendation are more likely to use a code reviewer recommendation system and that those who do not use a CRRS feel its adoption would add unnecessary complexity to their software development process. Most respondents felt positive about using a CRRS regarding coverage of code review tasks, precision of recommendations, and ease of use. However, more work is needed to create CRRSs that fit different team and organization sizes, provide more accurate results and broader scenario coverage, have diverse recommendations, and improve adaptability to coding styles.
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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 | 0.033 | 0.201 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.002 |
| 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".