AI-Assisted Formal Verification of Finite State Machines for Complex Software Systems

Abstract

Finite state machines (FSMs) offer a formal and structured model for software components that rely on discrete states, events, guards and transitions. But manual development and validation of FSMs for complex software systems is challenging as requirements can be incomplete, vague, and contradictory, and state spaces can quickly grow exponentially with concurrent components and data variables. This paper explores formal verification of FSMs using AI tools and techniques, which include machine learning, natural-language processing and automated reasoning techniques, for specification extraction, model construction, property generation, counterexample interpretation and model repair. The paper surveys the field of model checking, satisfiability modulo theories, temporal-logic specification and neural-network verification and reports that there are some gaps in scalability, specification quality, explainability and integration into industrial development workflows. A repeatable approach is suggested to measure the performance of AI-aided verification pipelines against manually created baselines. The paper concludes that AI can significantly decrease the manual effort required for verification but that models and properties generated by AI should still be subjected to formal analysis, human review and thorough validation prior to use in critical software applications in safety and security.Fuzzy clustering has been a well-known method for pattern discovery since it was first proposed.Fuzzy clustering is a well-known technique for pattern discovery since proposed that was popular before the advent of deep clustering techniques.