Distribution of Behaviour into Parallel Communicating Subsystems

The process of decomposing a complex system into simpler subsystems has been of interest to computer scientists over many decades, for instance, for the field of distributed computing. In this paper, motivated by the desire to distribute the process of active automata learning onto multiple subsystems, we study the equivalence between a system and the total behaviour of its decomposition which comprises subsystems with communication between them. We show synchronously- and asynchronously-communicating decompositions that maintain branching bisimilarity, and we prove that there is no decomposition operator that maintains divergence-preserving branching bisimilarity over all LTSs.Comment: In Proceedings EXPRESS/SOS 2019, arXiv:1908.0821

Active Learning of Decomposable Systems

Active automata learning is a technique of querying black box systems and modelling their behaviour. In this paper, we aim to apply active learning in parts. We formalise the conditions on systems---with a decomposable set of actions---that make learning in parts possible. The systems are themselves decomposable through non-intersecting subsets of actions. Learning these subsystems/components requires less time and resources. We prove that the technique works for both two components as well as an arbitrary number of components. We illustrate the usefulness of this technique through a classical example and through a real example from the industry

Pitfalls in applying model learning to industrial legacy software

Maintaining legacy software is one of the most common struggles of the software industry, being costly yet essential. We tackle that problem by providing better understanding of software by extracting behavioural models using the model learning technique. The used technique interacts with a running component and extracts abstract models that would help developers make better informed decisions. As promising in theory, as slippery in application it is, however. This report describes our experience in applying model learning to legacy software, and aims to prepare the newcomer for what shady pitfalls lie therein as well as provide the seasoned researcher with concrete cases and open problems. We narrate our experience in analysing certain legacy components at Philips Healthcare describing challenges faced, solutions implemented, and lessons learned