Empirical Study on Dependency-related License Violation in the JavaScript Package Ecosystem
Bibliographic record
Abstract
Open source software (OSS) is software whose source code can be reused under some particular terms and conditions. These terms and conditions are usually described by one or more software licenses written in the header part of the source files. A license may violate another one according to the terms and conditions. Making software by reusing OSS as dependency may cause dependency-related license violation if the developers overlook the license of the dependency. In this paper, we first conduct an empirical study on npm - a JavaScript-based software ecosystem - to study the prevalence of dependency-related license violation. The result suggests that only a few packages (0.644%) in npm have dependency-related license violations. However, we also observe that including the packages licensed under copyleft licenses in the dependency network potentially causes a high dependency-related license violation. We then conduct a preliminary questionnaire on the authors of packages detected as having dependency-related license violations to study the developers' attitudes. The results reveal: 1) the developers' overlooking and misunderstanding of the dependency-related license violations; 2) the difficulties in managing dependency-related license violations and the developers' demands for help.
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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 | 0.060 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".