Active Inference of Extended Finite State Machine Models with Registers and Guards

2026-04-23

AI Summary

This work addresses the challenge of reverse-engineering black-box systems that are model-free, non-resettable, and exhibit control flow dependent on internal state variables. The authors propose an active learning algorithm to infer extended finite-state machine (EFSM) models that accurately capture both data flow and control logic. Notably, this is the first method capable of effectively synthesizing EFSMs with registers and guard conditions without requiring system resets, thereby significantly relaxing assumptions about the system under test. Experimental results demonstrate that the approach achieves high-fidelity modeling of complex state-dependent systems, overcoming existing limitations in both system controllability and model expressiveness.

Technology Category

• Intelligent Robots: State Estimation
• Machine Learning: Active Learning
• Cognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

Extended finite state machines (EFSMs) model stateful systems with internal data variables and have numerous applications in software engineering. A major advantage of this type of model lies in its ability to model both the data flow and the data-dependent control behaviour. In the absence of such models, it is desirable to reverse-engineer them by observing the system's behaviour. However, existing approaches generally require the ability to reset the system during inference, or can only handle situations where the control flow depends exclusively on the input parameters, and not on the values of the stored data. In this work, we present a black-box active learning algorithm that infers EFSMs with guards and registers, and which significantly relaxes the assumptions that have to be made about the system in comparison to previous attempts.

Problem

• Extended Finite State Machine
• Active Inference
• Registers
• Guards
• Black-box Learning

Innovation

• active learning
• extended finite state machine
• register
• guard
• black-box inference