A First Look at Self-Admitted Miscommunications in GitHub Issues
Bibliographic record
Abstract
Effective communication is crucial for the success of open-source software development, particularly within distributed and asynchronous working environments on collaborative coding supporting platforms like GitHub. However, these environments often present significant communication challenges due to various factors, leading to project delays and wasted efforts. Despite prior research on collaboration challenges in software teams, there is a notable gap in understanding what, when, and where miscommunications occur in open-source projects. To address this gap, we mined 6,444 GitHub issues where developers explicitly admitted to miscommunication (using keywords such as "miscommunication") and manually analyzed the types, timing, and root causes of these self-admitted instances on a statistically significant sample set (363). Our findings are: (1) we developed a taxonomy of 12 issue types where miscommunications frequently occur, with bug reporting and feature requests being the most common; (2) we identified that most miscommunications occur before issue closure, but post-closure miscommunications, though less frequent, pose significant challenges; and (3) we uncovered five primary root causes of miscommunication, with technical misunderstandings being the most prevalent. This study provides the first comprehensive examination of miscommunication in open-source software development, offering insights and recommendations for researchers and practitioners to improve communication practices in GitHub issue discussions.
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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 | 0.011 | 0.110 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.015 | 0.010 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.001 | 0.006 |
| 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".