Abstract In 2015, LearnLib, the open-source framework for active automata learning, received the prestigious CAV artifact award. This paper presents the advancements made since then, highlighting significant additions to LearnLib, including state-of-the-art algorithms, novel learning paradigms, and increasingly expressive models. Our efforts to mature and maintain LearnLib have resulted in its widespread use among researchers and practitioners alike. A key factor in its success is the achieved compositionality which allows users to effortlessly construct thousands of customized learning processes tailored to their specific requirements. This paper illustrates these features through the development of a learning process for the life-long learning of procedural systems. This development can be easily replicated and modified using the latest public release of LearnLib.

Abstract This paper presents a compositional approach to active automata learning of Systems of Procedural Automata (SPAs), an extension of Deterministic Finite Automata (DFAs) to systems of DFAs that can mutually call each other. SPAs are of high practical relevance, as they allow one to efficiently learn intuitive recursive models of recursive programs after an easy instrumentation that makes calls and returns observable. Key to our approach is the simultaneous inference of individual DFAs for each of the involved procedures via expansion and projection: membership queries for the individual DFAs are expanded to membership queries of the entire SPA, and global counterexample traces are transformed into counterexamples for the DFAs of concerned procedures. This reduces the inference of SPAs to a simultaneous inference of the DFAs for the involved procedures for which we can utilize various existing regular learning algorithms. The inferred models are easy to understand and allow for an intuitive display of the procedural system under learning that reveals its recursive structure. We implemented the algorithm within the LearnLib framework in order to provide a ready-to-use tool for practical application which is publicly available on GitHub for experimentation.