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 <repository_url> cd <repository_folder> 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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".