Efficient Leverage of Symbolic ATG Tools to Advanced Coverage Criteria

TL;DR

This paper introduces label coverage, a new testing criterion for automatic test data generation that balances expressiveness and automation, supported by innovative techniques and optimizations for efficiency.

Contribution

It defines label coverage, proves its expressiveness, and develops efficient black-box techniques for ATG supporting advanced coverage criteria.

Findings

Label coverage is expressive and automatable.

Optimizations significantly reduce test generation costs.

Initial experiments show practical feasibility of the approach.

Abstract

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.