Artifact for (An L# Based Algorithm for Active Learning of Minimal Separating Automata) @CAV2026

This artifact contains two Dockers files that make it possible to reproduce the experiments described in the CAV'26 paper the "An L# Based Algorithm for Active Learning of Minimal Separating Automata". Abstract: A DFA separates two disjoint languages L1 and L2 if it accepts every word in l1 and rejects every word in L2. Algorithms for active learning of small separating DFAs have many applications, e.g., for learning network invariants, learning contextual assumptions in compositional verification, learning state machines from large amounts of log data, and learning bug pattern descriptions. We propose a new and simple learning algorithm, inspired by L#, that learns a minimal separating DFA for disjoint languages L1 and L2 if one exists. Experiments show that our algorithm significantly outperforms existing active learning algorithms on both randomly generated and industrial benchmarks.