Open Source-Style Collaborative Development Practices in Commercial Projects Using GitHub
Bibliographic record
Abstract
Researchers are currently drawn to study projects hosted on GitHub due to its popularity, ease of obtaining data, and its distinctive built-in social features. GitHub has been found to create a transparent development environment, which together with a pull request-based workflow, provides a lightweight mechanism for committing, reviewing and managing code changes. These features impact how GitHub is used and the benefits it provides to teams' development and collaboration. While most of the evidence we have is from GitHub's use in open source software (OSS) projects, GitHub is also used in an increasing number of commercial projects. It is unknown how GitHub supports these projects given that GitHub's workflow model does not intuitively fit the commercial development way of working. In this paper, we report findings from an online survey and interviews with GitHub users on how GitHub is used for collaboration in commercial projects. We found that many commercial projects adopted practices that are more typical of OSS projects including reduced communication, more independent work, and self-organization. We discuss how GitHub's transparency and popular workflow can promote open collaboration, allowing organizations to increase code reuse and promote knowledge sharing across their teams.
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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 | 0.019 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".