Evolutionary improvement of assertion oracles

Assertion oracles are executable boolean expressions placed inside the program that should pass (return true) for all correct executions and fail (return false) for all incorrect executions. Because designing perfect assertion oracles is difficult, assertions often fail to distinguish between correct and incorrect executions. In other words, they are prone to false positives and false negatives. In this paper, we propose GAssert (Genetic ASSERTion improvement), the first technique to automatically improve assertion oracles. Given an assertion oracle and evidence of false positives and false negatives, GAssert implements a novel co-evolutionary algorithm that explores the space of possible assertions to identify one with fewer false positives and false negatives. Our empirical evaluation on 34 Java methods from 7 different Java code bases shows that GAssert effectively improves assertion oracles. GAssert outperforms two baselines (random and invariant-based oracle improvement), and is comparable with and in some cases even outperformed human-improved assertions.