Test Case Generation from Graph Transformation Systems Using Deep Reinforcement Learning

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Simin Ghasemi, Vahid Rafe, Mohammad Mehrabi, Reiko Heckel, Issam Al-Azzoni

Published: 2025, Last Modified: 07 May 2026ICGT 2025EveryoneRevisionsBibTeXCC BY-SA 4.0
Abstract: Graph transformations can be used to specify and analyse software systems by modelling operations as rules and generating the labelled transition system (LTS) as a representation of system behaviour. Model-based testing (MBT) often uses model checking over LTS to discover paths that satisfy certain test requirements. Significant challenges include the size of the state space and the complexity of model checking. Meta-heuristic search-based approaches try to cope with this problem by exploring only a small portion of the LTS to produce paths that cover maximal test objectives. Despite acceptable results in small case studies, these approaches also do not scale well.