On the Detection of Licenses Violations in the Android Ecosystem
Bibliographic record
Abstract
Mobile applications (apps) developers often reuse code from existing libraries and frameworks in order to reduce development costs. However, these libraries and frameworks are governed by licenses to which developers must comply. A failure to comply with a license is likely to result in penalties and fines. In this paper, we analyse the licenses of 857 mobile apps from the F-droid market with the aim to understand the types of licenses that are mostly used by developers of open-source mobile apps and how these licenses evolve over time. We also investigate licenses violations and the evolution of these violations over time. Results show that developers of open-source mobile apps mostly use GPL and Apache licenses. We found licenses violations in 17 out of 857 apps, and 7 apps still had violations in their latest release at the time of this study. We also observed that many files are not licensed in their first release. Developers seem to have some difficulties understanding the legal constraints of licenses' terms.
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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 | 0.000 | 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".