2026-10-08T03:53:35Z https://api.figshare.com/v2/oai

oai:figshare.com:article/30999184 2026-09-22T08:08:00Z category_29182 item_type_3 month_year_09_2026

Forecasting the Maintained Score from the OpenSSF Scorecard: A Study of GitHub Repositories Linked to PyPI Packages Alexandros Tsakpinis (17863247) Efe Berk Ergüleç (23565539) Emil Schwenger (23565517) Alexander Pretschner (9241805) Empirical software engineering Maintenance Activities Open-Source Software Time Series Forecasting <h2 dir="ltr">Overview</h2><p dir="ltr">This replication package accompanies the paper <b>“</b><b>Forecasting the Maintained Score from the OpenSSF Scorecard: A Study of GitHub Repositories Linked to PyPI Packages</b><b>”</b>. It contains all code, configuration files, input datasets, and generated outputs required to reproduce the study’s experiments on forecasting the OpenSSF Maintained score for GitHub Repositories linked to PyPI libraries.</p><p dir="ltr"><b>Note:</b> The replication package is distributed in ZIP-compressed form, as the total size of the uncompressed contents is approximately 10.5 GB.</p><h2 dir="ltr">Contents</h2><ul><li><code><strong>00_testing/</strong></code><br>End-to-end experimental pipeline covering repository filtering, maintenance score computation, data processing and sampling, time-series analysis, and plotting. Experiments are driven by configurable JSON setups.</li><li><code><strong>01_input/</strong></code><br>Input datasets, including:</li><li><ul><li>Aggregated PyPI and GitHub ecosystem metrics (JSON)</li><li>Preprocessed relational dataset with computed maintenance scores (Parquet)</li></ul></li><li><code><strong>05_implementation/</strong></code><br>Model implementations and orchestration logic, including:</li><li><ul><li>Random Forest</li><li>Long Short-Term Memory (LSTM)<br>This directory also contains shared utilities and training/evaluation workflows.</li></ul></li><li><code><strong>06_results/</strong></code><br>Reproducible outputs such as JSON metrics, averaged and iterated confusion matrices, visualizations, and analysis notebooks.</li></ul><h2 dir="ltr">Reproduction Instructions</h2><ol><li>Create a virtual environment and install dependencies from <code>requirements.txt</code>.</li><li>Run experiments via <code>00_testing/main.py</code> using a selected configuration file, for example:<pre><pre>python 00_testing/main.py --config 00_testing/config/complete_lstm.json</pre></pre></li><li>Results are written to <code>06_results/</code> as JSON files and plots.</li></ol><p dir="ltr">Configuration files allow control over:</p><ul><li>Sampling strategies (unbalanced, balanced, hybrid)</li><li>Forecasting type (single-step, multi-step iterative, multi-step direct)</li><li>Sequence lengths</li><li>Time windows</li></ul><h2 dir="ltr">Models and Evaluation</h2><ul><li><b>Models</b></li><li><ul><li>Random Forest</li><li>LSTM</li></ul></li><li><b>Prediction Targets</b></li><li><ul><li><i>Daily mode</i>: absolute maintenance score (0–10)</li><li><i>Slope mode</i>: month-to-month change in maintenance score (−10 to +10)</li></ul></li><li><b>Evaluation Metrics</b></li><li><ul><li>Regression: MSE, RMSE, MAE, R²</li><li>Classification: accuracy, precision, recall, F1</li><li>Confusion matrices aggregated across iterations and parameter grids</li></ul></li></ul><h2 dir="ltr">Data Notes</h2><ul><li>Input JSON files aggregate ecosystem activity and repository-level metrics.</li><li>Parquet files provide the prepared relational dataset with computed maintenance scores.</li><li>Notebooks in <code>06_results/</code> document analyses (e.g., boxplots, bucketed confusion matrices and heatmaps) corresponding to figures presented in the paper.</li></ul><p><br></p> 2026-09-22T08:08:00Z Dataset Dataset 10.6084/m9.figshare.30999184.v1 https://figshare.com/articles/dataset/Forecasting_the_Maintained_Score_from_the_OpenSSF_Scorecard_A_Study_of_GitHub_Repositories_Linked_to_PyPI_Packages/30999184 CC BY 4.0