Do Words Have Power? Understanding and Fostering Civility in Code Review Discussion
Bibliographic record
Abstract
Modern Code Review (MCR) is an integral part of the software development process where developers improve product quality through collaborative discussions. Unfortunately, these discussions can sometimes become heated by the presence of inappropriate behaviors such as personal attacks, insults, disrespectful comments, and derogatory conduct, often referred to as incivility. While researchers have extensively explored such incivility in various public domains, our understanding of its causes, consequences, and courses of action remains limited within the professional context of software development, specifically within code review discussions. To bridge this gap, our study draws upon the experience of 171 professional software developers representing diverse development practices across different geographical regions. Our findings reveal that more than half of these developers 56.72 % have encountered instances of workplace incivility, and a substantial portion of that group 83.70 % reported experiencing such incidents at least once a month. We also identified various causes, positive and negative consequences, and potential courses of action for uncivil communication. Moreover, to address the negative aspects of incivility, we propose a model for promoting civility that detects uncivil comments during communication and provides alternative civil suggestions while preserving the original comments’ semantics, enabling developers to engage in respectful and constructive discussions. An in-depth analysis of 2K uncivil review comments using eight different evaluation metrics and a manual evaluation suggested that our proposed approach could generate civil alternatives significantly compared to the state-of-the-art politeness and detoxification models. Moreover, a survey involving 36 developers who used our civility model reported its effectiveness in enhancing online development interactions, fostering better relationships, increasing contributor involvement, and expediting development processes. Our research is a pioneer in generating civil alternatives for uncivil discussions in software development, opening new avenues for research in collaboration and communication within the software engineering context.
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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 | 0.066 | 0.294 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.003 |
| Science and technology studies | 0.023 | 0.062 |
| Scholarly communication | 0.028 | 0.045 |
| Open science | 0.003 | 0.030 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.005 | 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".