Early Identification of Future Committers in Open Source Software Projects
Bibliographic record
Abstract
There exists two types of developers in Open Source Software (OSS) projects: 1) Committers who have permission to commit edited source code to the Version Control System (VCS), 2) Developers who contribute source code but cannot commit to the VCS directly. In order to develop and evolve high quality OSS, projects are always in search of new committers. OSS projects often promote strong developers to become committers. When existing committers find strong developers, they propose their promotion to a committer role. Delaying the committer-promotion might lead to strong developers departing from an OSS project and the project losing them. However early committer-promotion comes with its own slew of risks as well (e.g., the promotion of inexperienced developers). Hence, committer-promotion decisions are critical for the quality and successful evolution of OSS projects. In this paper, we examine the committer-promotion phenomena for two OSS projects (Eclipse and Firefox). We find that the amount of activities by future committers was higher than the amount of activities by developers who did not become committers). We also find that some developers are promoted to a committer role very rapidly (within a few month) while some of developers take over one year to become a committer. Finally, we develop a committer-identification model to assist OSS projects identifying future committers.
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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 | 0.008 | 0.076 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".