Counterexample-Driven Genetic Programming: Stochastic Synthesis of Provably Correct Programs

Genetic programming is an effective technique for inductive synthesis of programs from tests, i.e. training examples of desired input-output behavior. Programs synthesized in this way are not guaranteed to generalize beyond the training set, which is unacceptable in many applications. We present Counterexample-Driven Genetic Programming (CDGP) that employs evolutionary search to synthesize provably correct programs from formal specifications. CDGP employs a Satisfiability Modulo Theories (SMT) solver to formally verify programs in the evaluation phase. A failed verification produces counterexamples that are in turn used to calculate fitness and thereby drive the search process. When compared with a range of approaches on a suite of state-of-the-art specification-based synthesis benchmarks, CDGP systematically outperforms them, typically synthesizing correct programs faster and using fewer tests.