Model-based, Mutation-driven Test-case Generation Via Heuristic-guided Branching Search

摘要

This work introduces a heuristic-guided branching search algorithm for model-based, mutation-driven test-case generation. The algorithm is designed towards the efficient and computationally tractable exploration of discrete, non-deterministic models with huge state spaces. Asynchronous parallel processing is a key feature of the algorithm. The algorithm is inspired by the successful path planning algorithm Rapidly exploring Random Trees (RRT). We adapt RRT in several aspects towards test-case generation. Most notably, we introduce parametrized heuristics for start and successor state selection, as well as a mechanism to construct test cases from the data produced during the search.