Understanding the Popularity of Packages in Maven Ecosystem
Bibliographic record
Abstract
The widespread availability of open-source software packages in ecosystems like Maven has significantly improved developer productivity by promoting the reuse of pre-existing packages. However, the vast number of available packages often poses challenges in selecting suitable packages. This study investigates the role of popularity metrics in evaluating Maven packages by analyzing 103,315 packages, each at least two years old. Metrics were collected from the Maven Neo4j dataset and GitHub repositories to examine their relationships and importance in determining package popularity. Our analysis reveals strong interdependencies among community-driven GitHub metrics, such as stars, forks, pull requests, and contributors, which highlight their role in defining package popularity. Conversely, Maven-specific metrics, including dependencies and vulnerabilities, showed weak correlations with GitHub-based popularity indicators. Our analysis identified license status, commits count, presence of README files, and usages as the most significant predictors of package popularity, while vulnerabilities had limited statistical impact. These findings underscore the complementary nature of technical and community-driven metrics in assessing package popularity and provide actionable insights for developers and researchers to better evaluate and select open-source software packages.
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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 | 0.002 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".