Automated Error-Detection and Repair for Compositional Software Specifications

Abstract

The complexity of error diagnosis in requirements specifications, already high, is increased when requirements refer to various system components, on whose interaction the system’s aims depend. Further, finding causes of error, and ways of overcoming them, cannot easily be achieved without a systematic methodology. This has led researchers to explore the combined use of verification and machine-learning to support automated software analysis and repair. However, existing approaches have been limited by using formalisms in which modularity and compositionality cannot be explicitly expressed. In this paper we overcome this limitation. We define a translation from a representative process algebra, Finite State Processes, into the action language $\mathcal{C}+$. This enables forms of verification not supported by previous methods. We then use a logic-programming equivalent of $\mathcal{C}+$, to which we apply inductive logic programming for learning repairs to system components while ensuring no new errors are introduced and interactions with other components are maintained. These two phases are iterated until a correct specification is reached, enabling rigorous and scalable support for automated analysis and repair of component-based specifications.