What is Formal Verification without Specifications? A Survey on mining LTL Specifications

TL;DR

This survey reviews recent methods for automatically mining Linear Temporal Logic (LTL) specifications from system behavior examples, addressing the challenge of manual specification formulation in formal verification.

Contribution

It provides a comprehensive comparison of recent techniques for learning LTL specifications, highlighting diverse approaches and their applicability.

Findings

Multiple techniques use constraint solving, neural networks, and search methods.

Approaches vary in handling different aspects of specification mining.

The survey aids practitioners in selecting suitable methods.

Abstract

Virtually all verification techniques using formal methods rely on the availability of a formal specification, which describes the design requirements precisely. However, formulating specifications remains a manual task that is notoriously challenging and error-prone. To address this bottleneck in formal verification, recent research has thus focussed on automatically generating specifications for formal verification from examples of (desired and undesired) system behavior. In this survey, we list and compare recent advances in mining specifications in Linear Temporal Logic (LTL), the de facto standard specification language for reactive systems. Several approaches have been designed for learning LTL formulas, which address different aspects and settings of specification design. Moreover, the approaches rely on a diverse range of techniques such as constraint solving, neural network training, enumerative search, etc. We survey the current state-of-the-art techniques and compare them for the convenience of the formal methods practitioners.