Spring 2021

Data-Driven Inference of Representation Invariants

Monday, Mar. 1, 2021 8:30 am9:00 am

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representation invariant is a property that holds of all values of abstract type produced by a module. Representation invariants play important roles in software engineering and program verification.  I will present a counterexample-driven algorithm for inferring a representation invariant that is sufficient to imply a desired specification for a module. The key novelty is a type-directed notion of visible inductiveness, which ensures that the algorithm makes progress toward its goal as it alternates between weakening and strengthening candidate invariants. The algorithm is parameterized by an example-based synthesis engine and a verifier, and we prove that it is sound and complete for first-order modules over finite types, assuming that the synthesizer and verifier are as well. We implement these ideas in a tool called Hanoi, which synthesizes representation invariants for recursive data types, including data types with higher-order functions.  Joint work with Anders Miltner, Saswat Padhi, and David Walker.