Software bugs remain a compelling problem. Automated program repair is a promising approach for reducing cost, and many methods have recently demonstrated positive results. However, success on any particular bug is variable, as is the cost to find a repair. This paper focuses on generate-and-validate repair methods that enumerate candidate repairs and use test cases to define correct behavior. We formalize repair cost in terms of test executions, which dominate most test-based repair

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... Insights from this model lead to a novel deterministic repair algorithm that computes a patch quotient space with respect to an approximate semantic equivalence relation. This allows syntactic and dataflow analysis techniques to dramatically reduce the repair search space. Generate-and-validate program repair is shown to be a dual of mutation testing, directly suggesting possible cross-fertilization. Evaluating on 105 realworld bugs in programs totaling 5MLOC and involving 10,000 tests, our new algorithm requires an order-of-magnitude fewer test evaluations than the previous state-of-the-art and is over five times more efficient monetarily.