Towards Improving Automated Testing with GraphWalker

GraphWalker is a widespread automated model-based testing tool that generates executable test cases from graph models of a system under test. GraphWalker implements only two random test generation algorithms and has no optimization-based algorithm, where an evaluation of its performance in test lengths and coverage remains an open question in the literature. In this work, we performed experiments on three realistic systems to evaluate redundancy, coverage, and length of GraphWalker test cases. The experimental results show that even the best GraphWalker test cases are highly redundant, limited in edge pair coverage, and need significantly longer test cases to increase edge pair coverage. Overall, we establish a baseline to compare future optimization-based algorithms with, while the amount of improvement and its impact are important research questions for the future.