Replication Package for "Beyond the YAML File: Understanding Real-World GitHub Actions Workflow Adoption" (submitted to EASE 2026)

This repository contains the replication package for the study "Beyond the YAML File: Understanding Real-World GitHub Actions Workflow Adoption" submitted to EASE 2026 conference. It provides the code, data artifacts, and instructions necessary to reproduce and access the quantitative and qualitative analyses from the paper. Repository structure - data-pipelines-and-analysis/ - data_pipeline/: End-to-end data collection, storage, and analysis pipeline. - collect_data.py: Entry point to collect data from the GitHub API. - config/: Configuration (API tokens, runtime options). - crawlers/: Modular crawlers for repositories, commits, pull requests, workflow runs, and jobs. - database/: DB connection and ORM-like models used during collection. - persistence/: Storage interfaces and stores. - data/: Lightweight helpers and logs for local data handling. - analysis/: Reproduction scripts for figures, tables, and statistics used in the paper. Includes figures/ and intermediate data/ CSVs used by scripts. - services/ and scripts/: Utilities and checks (e.g., data quality scripts). - README.md: Detailed setup, configuration, and execution steps for the pipeline and analyses. - manual_and_qualitative/: CSVs and notes for manual and qualitative analysis (e.g., RQ1). See its README.md for details and suggested usage. Quick start Reproducing the pipeline and analysis: 1. Navigate to data-pipelines-and-analysis/data_pipeline/.2. Create and activate a virtual environment.3. Install requirements: pip install -r requirements.txt4. Follow the instructions in data-pipelines-and-analysis/data_pipeline/README.md to configure credentials, run data collection, and execute analysis scripts in analysis/. For qualitative artifacts and manual coding resources, see data-pipelines-and-analysis/manual_and_qualitative/README.md. Reproducing figures and tables The analysis/ folder inside data_pipeline/ contains scripts that generate the figures and summary tables used in the paper. Many scripts read from analysis/data/ and write outputs to analysis/figures/. Refer to each script's docstring and the data_pipeline/README.md.