Survey on Learning-Based Formal Methods: Taxonomy, Applications and Possible Future Directions

Abstract

Formal methods play an important role in testing and verifying software quality, especially in modern society with rapid technological updates. Learning-based techniques have been extensively applied to learn (a model or model-free) for formal verification and to learn system specifications, and resulted in numerous contributions. Due to the fact that adequate system models are often difficult to design manually and manual definition of specifications for such software systems gets infeasible, which motivate new research directions in learning models and/or specifications from observed system behaviors automatically. This paper mainly concentrates on learning-based techniques in formal methods area. An up-to-date overview of the current state-of-the-art in learning-based formal methods is provided in the paper. This paper is not a comprehensive survey of learning-based techniques in formal methods area, but rather as a survey of the taxonomy, applications and possible future directions in learning-based formal methods.

A Survey on Formal Verification and Validation Techniques for Internet of Things

“…In this section, we provide a concise overview of the most-prevalent forms of formal techniques currently available to the research community [15,16] (Figure 2):…”

“…To ensure the reliability, security, and safety of IoT systems, it is crucial to apply FV&V techniques. Formal methods use mathematical techniques to model and analyze systems rigorously [15][16][17]. As illustrated in Figure 1, they allow system designers to specify the behavior and properties of the system using precise mathematical notations, such as logic formulas and state machines.…”

Formal XAI via Syntax-Guided Synthesis

“…Several works exist to make the decision-making of black-box systems used for control explainable. Automata learning refers to techniques that infer a surrogate model (e.g., in the form of an input-output automaton [41], a timed automaton [13] or an MDP [38]) from a given black-box system by observing its behavior. The tool dtControl [5] learns decision trees for hybrid and probabilistic control systems, and has been recently extended to support richer algebraic predicates as splitting rules with the use of support vector machines [22].…”