Efficient Leverage of Symbolic ATG Tools to Advanced Coverage Criteria
This work addresses software engineers by providing an incremental improvement in ATG tools to support more advanced coverage criteria efficiently.
The paper tackled the gap between symbolic automatic test data generation (ATG) tools and advanced coverage criteria by defining a new label coverage criterion, proving it expressive and automatable, and developing efficient techniques to avoid exponential search space blow-up, with initial experiments showing reasonable cost and significant savings from optimizations.
Automatic test data generation (ATG) is a major topic in software engineering. In this paper, we seek to bridge the gap between the coverage criteria supported by symbolic ATG tools and the most advanced coverage criteria found in the literature. We define a new testing criterion, label coverage, and prove it to be both expressive and amenable to efficient automation. We propose several innovative techniques resulting in an effective black-box support for label coverage, while a direct approach induces an exponential blow-up of the search space. Initial experiments show that ATG for label coverage can be achieved at a reasonable cost and that our optimisations yield very significant savings.