Understanding the Popularity of Packages in Maven Ecosystem
Bibliographic record
Abstract
Maven Package Analysis Overview This repository contains scripts and notebooks for analyzing Maven packages. The analysis includes: Distribution of Maven packages across different ranges of star counts. Correlation matrix of popularity metrics for Maven packages. Comparison of features across the top and bottom 20% of packages, along with P-value and Cohen’s d for effect size. Hierarchical clustering to handle multi-collinearity among features, with selected metrics listed. Logistic regression analysis to generate final results. Prerequisites Python 3.8 or higher All required Python packages listed in requirements.txt Installation Clone the repository: git clone cd Install the required dependencies: pip install -r requirements.txt Usage 1. Distribution of Maven Packages by Star Count To find the distribution of Maven packages across different ranges of star counts, run the following command: python .\star_count_distribution.py 2. Correlation Matrix of Popularity Metrics To generate the correlation matrix for Maven package popularity metrics, run the following command: python .\cluster_corelation.py 3. Feature Comparison Across Top and Bottom 20% To compare features across the top and bottom 20% of packages, including P-value and Cohen’s d for effect size, run the following command: python .\minmaxmedian.py 4. Hierarchical Clustering for Feature Selection To apply hierarchical clustering and handle multi-collinearity among features: Open hierarchical_clustering.ipynb in a Jupyter Notebook environment. Run all the cells in the notebook. This step will identify the following metrics: License Commits Count Readme Exists About Info Dependencies Usages Closed Issues Percentage Release Frequency Vulnerabilities Figure 3 will also be generated during this process. 5. Logistic Regression Analysis To perform logistic regression analysis and generate the final results, run the following command: python .\Logistic_Regression.py Contact For any questions or issues, please reach out to the repository maintainer.
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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 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.009 | 0.016 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.030 | 0.026 |
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".