Deriving Invariants by Algorithmic Learning, Decision Procedures, and Predicate Abstraction Seminars/Workshops
|Place:||Room 409, Bldg 302, SNU|
By combining algorithmic learning, decision procedures, and predicate abstraction, we present an automated technique for finding loop invariants in propositional formulae. Given invariant approximations derived from pre- and post-conditions, our new technique exploits the flexibility in invariants by a simple randomized mechanism. The proposed technique is able to generate invariants for some Linux device drivers and SPEC2000 benchmarks in our experiments.
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