Specifications are necessary for nearly every software engineering task, but they are often missing or incomplete. "Specification mining" is a line of research promising to solve this problem through automated tools that infer specifications directly from existing programs. The standard practice is one of inductive learning: mining tools make observations about software and inductively generalize them into specifications. Inductive reasoning is unsound, however, and existing tools commonly

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... le with the problem of inferring "false" specifications, which must be manually checked. In this work, we introduce a new technique for automatically validating mined specifications that lessens this manual burden. Our technique is not based on heuristics; it rather uses a general, semantic definition of a "true" specification. We perform systematic, targeted program transformations to test a mined specification's necessity for overall correctness. If a "violating" program is correct, the specification is false. We have implemented our technique in a prototype tool that validates temporal properties of Java programs, and we demonstrate it to be effective through a large-scale case study on the DaCapo benchmarks.