What is Formal Verification without Specifications? A Survey on mining LTL Specifications
This survey addresses the problem of automating specification formulation for formal verification practitioners, but it is incremental as it compiles existing research without introducing new methods.
The paper surveys recent advances in automatically generating Linear Temporal Logic (LTL) specifications from examples of system behavior to address the manual and error-prone bottleneck in formal verification, comparing techniques like constraint solving and neural networks for practitioners.
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.