Error-awareness Accelerates Active Automata Learning: Supplementary Material

Supplementary material for the paper: Error-awareness Accelerates Active Automata Learning, accepted to FM26. This artifact includes the code for all L#e configurations and the adapted test suite generation methods.It also contains all the code and benchmarks required to reproduce the Figures and Tables in the paper.In addition, it offers instructions for applying the code to new benchmarks or running it with alternative settings.The code is built on top of AALpy, a Python library for active automata learning. We provide scripts for running all experiments.When tested on a high-end laptop, the reduced version of the experiments takes about two hours.In comparison with the full experiments, we reduced the number of seeds for the ASML and Monitor benchmarks in Experiment 1 from 30 to 5. The artifact has no special system requirements; 8 GB of RAM is sufficient. This artifact is structured as follows:1. Setup instructions for both Docker and local execution, including a quick smoke test.2. Steps to reproduce all experiments and plots from the paper.3. An overview of the repository structure.4. A description of the included benchmarks.5. Guidance on how to use the artifact for additional experiments.