Dependent input sampling strategies

Abstract

Understanding extreme execution times is of great importance in gaining assurance in real-time embedded systems. The standard benchmark for dynamic testingÐuniform randomised testingÐis inadequate for reaching extreme execution times in these systems. Metaheuristics have been shown to be an efective means of directly searching for inputs with such behaviours but the increasing complexity of modern systems is now posing challenges to the efectiveness of this approach. The research reported in this paper investigates the use of metaheuristic search to discover biased random sampling regimes. Rather than search for test inputs, we search for distributions of test inputs that are then sampled. The search proceeds to discover and exploit relationships between test input variables, leading to sampling regimes where the distribution of a sampled parameter depends on the values of previously sampled input parameters. Our results show that test vectors indirectly generated from our dependent approach produce signiicantly more extreme (longer) execution times than those generated by direct metaheuristic searches. CCS CONCEPTS • Theory of computation → Optimization with randomized search heuristics; • Software and its engineering → Software testing and debugging; Search-based software engineering; • Computer systems organization → Multicore architectures;