Variability-Aware Event Sequence Graphs for Scalable Software Product Line Testing

This archive contains the raw experimental data produced by the distributed evaluation in the manuscript "Variability-Aware Event Sequence Graphs for Scalable Software Product Line Testing." It accompanies the source-code repository at https://github.com/esg4aspl/esg-with-feature-expressions, which holds the implementation, the orchestration scripts, the feature-model inputs, and the analysis pipeline.

A 10-node, 80-core DigitalOcean cluster generated the data over approximately 2,440 droplet-hours, evaluating the proposed Model Once, Generate Any approach against two baselines — a Structural Baseline (Event Flow Graph testing via the GUITAR framework) and a Stochastic Baseline (Random Walk traversal) — across eight Software Product Lines ranging from 12 valid products to 124 billion valid products: Soda Vending Machine, eMail, Elevator, Bank Account, Student Attendance System, Tesla, syngo.via, and Hockerty Shirts. For each Software Product Line, the archive contains per-product DOT graph files, per-product Event Flow Graphs, the GUITAR test sequences for the Structural Baseline, and the per-shard measurement CSV files for the three research questions on comparative efficiency, extreme scalability, and fault detection. The per-product test sequences produced by ESG-Fx and Random Walk are intentionally excluded — the manuscript's quantitative claims rely on the aggregated CSV measurements, and these sequences are regenerable by re-running RQ1 on the cluster.

The tarball mirrors the directory layout of files/Cases/ in the source repository and is intended to be extracted in place over that directory. After extraction, the Python analysis pipeline in the source repository runs without modification and reproduces the manuscript's tables and figures. Detailed reproduction instructions are in REPRODUCIBILITY.md in the source repository at https://github.com/esg4aspl/esg-with-feature-expressions/blob/main/REPRODUCIBILITY.md