License usage and changes: a large-scale study of Java projects on GitHub
Bibliographic record
Abstract
Abstract—Software licenses determine, from a legal point of view, under which conditions software can be integrated, used, and above all, redistributed. Licenses evolve over time to meet the needs of development communities and to cope with emerging legal issues and new development paradigms. Such evolution of licenses is likely to be accompanied by changes in the way how software uses such licenses, resulting in some licenses being adopted while others are abandoned. This paper reports a large empirical study aimed at quantitatively and qualitatively investigating when and why developer change software licenses. Specifically, we first identify licenses ’ changes in 1,731,828 commits, representing the entire history of 16,221 Java projects hosted on GitHub. Then, to understand the rationale of license changes, we perform a qualitative analysis—following a grounded theory approach—of commit notes and issue tracker discussions concerning licensing topics and, whenever possible, try to build traceability links between discussions and changes. Our results point out a lack of traceability of when and why licensing changes are made. This can be a major concern, because a change in the license of a system can negatively impact those that reuse it.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".