An Industrial Investigation of Similarity Measures for Model-Based Test Case Selection

Applying model-based testing (MBT) in practice requires practical solutions for scaling up to large industrial systems. One challenge that we have faced while applying MBT was the generation of test suites that were too large to be practical, even for simple coverage criteria. The goal of test case selection techniques is to select a subset of the generated test suite that satisfies resource constraints while yielding a maximum fault detection rate. One interesting heuristic is to choose the most diverse test cases based on a pre-defined similarity measure. In this paper, we investigate and compare possible similarity functions to support similarity-based test selection in the context of state machine testing, which is the most common form of MBT. We apply the proposed similarity measures and a selection strategy based on genetic algorithms to an industrial software system. We compare their fault detection rate based on actual faults. The results show that applying Jaccard Index on test cases represented as a set of trigger-guards is the most cost-effective similarity measure. We also discuss the overall benefits of our test selection approach in terms of test execution savings.