Preprocessed data for find motives behind cross-platform forks from SWHGD dataset
Bibliographic record
Abstract
The fork-based development mechanism provides the flexibilityand the unified processes for software teams to collaborate easilyin a distributed setting without too much coordination overhead.Currently, multiple social coding platforms support fork-based de-velopment, such as GitHub, GitLab, and Bitbucket. Although thesedifferent platforms virtually share the same features, they havedifferent emphasis. As GitHub is the most popular platform and thecorresponding data is publicly available, most of the current stud-ies are focusing on GitHub hosted projects. However, we observedanecdote evidences that people are confused about choosing amongthese platforms, and some projects are migrating from one platformto another, and the reasons behind these activities remain unknown.With the advances of Software Heritage Graph Dataset (SWHGD),we have the opportunity to investigate the forking activities acrossplatforms. In this paper, we conduct an exploratory study on 10popular open-source projects to identify cross-platform forks andinvestigate the motivation behind. Preliminary result shows thatcross-platform forks do exist, for the 10 subject systems in thisstudy, we found 81,357 forks in total among which 179 forks areon GitLab. Based on our qualitative analysis we found that most ofthe cross-platform forks that we identified are mirrors of anotherplatform, but we still find cases that were created due to preferenceof using certain functionalities (e.g. Continuous Integration(CI))supported by different platforms. This study lays the foundation offuture research directions, such as understanding the differencesbetween platforms and supporting cross-platform collaboration.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.014 | 0.011 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.008 |
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; both teacher heads agree on what is shown here.
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".