Automated Specification Discovery via User-Defined Predicates

Abstract

Automated discovery of specifications for heap-manipulating programs is a challenging task due to the complexity of aliasing and mutability of data structures. This task is further complicated by an expressive domain that combines shape, numerical and bag information. In this paper, we propose a compositional analysis framework in the presence of user-defined predicates, which would derive the summary for each method in the expressive abstract domain, independently from its callers. We propose a novel abstraction method with a bi-abduction technique in the combined domain to discover pre-/post-conditions that could not be automatically inferred before. The analysis does not only prove the memory safety properties, but also finds relationships between pure and shape domains towards full functional correctness of programs. A prototype of the framework has been implemented and initial experiments have shown that our approach can discover interesting properties for non-trivial programs.KeywordsSymbolic ExecutionAbstract DomainBinary Search TreeSeparation LogicInput ListThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.