We Are Family: Analyzing Communication in GitHub Software Repositories and Their Forks
Bibliographic record
Abstract
GitHub facilitates software development practices that encourage collaboration and communication. Part of GitHub's model includes forking, which enables users to make changes on a copy of the base repository. The process of forking opens avenues of communication between the users from the base repository and the users from the forked repositories. Since forking on GitHub is a common mechanism for initiating repositories, we are interested in how communication between a repository and its forks (forming a software family) relates to stars. In this paper, we study communications within 385 software families comprised of 13,431 software repositories. We find that the fork depth, the number of users who have contributed to multiple repositories in the same family, the number of followers from outside the family, familial pull requests, and reported issues share a statistically significant relationship with repository stars. Due to the importance of issues and pull requests, we identify and compare common topics in issues and pull requests from inside the repository (via branching) and within the family (via forking). Our results offer insights into the importance of communication within a software family, and how this leads to higher individual repository star counts.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".