Release conventions of open‐source software: An exploratory study
Bibliographic record
Abstract
Abstract Software engineering (SE) methodologies are widely used in both academia and industry to manage the software development life cycle. A number of studies of SE methodologies involve interviewing stakeholders to explore the real‐world practice. Although these interview‐based studies provide us with a user's perspective of an organization's practice, they do not describe the concrete summary of releases in open‐source social coding platforms. In particular, no existing studies investigated how releases are evolved in open‐source coding platforms, which assist release planners to a large extent. This study explores software development patterns followed in open‐source projects to see the overall management's reflection on software release decisions rather than concentrating on a particular methodology. Our experiments on 51 software origins (with 1777k revisions and 12k releases) from the Software Heritage Graph Dataset (SWHGD) and their GitHub project boards (with 23k cards) reveal reasonably active project management with phase simplicity can release software versions more frequently and can follow the small release conventions of Extreme Programming. Additionally, the study also reveals that a combination of development and management activities can be applied to predict the possible number of software releases in a month ( ).
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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 | 0.007 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 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".