Automated Test Oracles for Flaky Cyber-Physical System Simulators: Approach and Evaluation (2508.20902v1)

Abstract: Simulation-based testing of cyber-physical systems (CPS) is costly due to the time-consuming execution of CPS simulators. In addition, CPS simulators may be flaky, leading to inconsistent test outcomes and requiring repeated test re-execution for reliable test verdicts. Automated test oracles that do not require system execution are therefore crucial for reducing testing costs. Ideally, such test oracles should be interpretable to facilitate human understanding of test verdicts, and they must be robust against the potential flakiness of CPS simulators. In this article, we propose assertion-based test oracles for CPS as sets of logical and arithmetic predicates defined over the inputs of the system under test. Given a test input, our assertion-based test oracle determines, without requiring test execution, whether the test passes, fails, or if the oracle is inconclusive in predicting a verdict. We describe two methods for generating assertion-based test oracles: one using genetic programming~(GP) that employs well-known spectrum-based fault localization (SBFL) ranking formulas, namely Ochiai, Tarantula, and Naish, as fitness functions; and the other using decision trees (DT) and decision rules (DR). We evaluate our assertion-based test oracles through case studies in the domains of aerospace, networking and autonomous driving. We show that test oracles generated using GP with Ochiai are significantly more accurate than those obtained using GP with Tarantula and Naish or using DT or DR. Moreover, this accuracy advantage remains even when accounting for the flakiness of the system under test. We further show that the assertion-based test oracles generated by GP with Ochiai are robust against flakiness with only 4% average variation in their accuracy results across four different network and autonomous driving systems with flaky behaviours.