Semi-Automatic Repair of Over-Constrained Models for Combinatorial Robustness Testing

Abstract

Combinatorial robustness testing is an approach to generate separate test inputs for positive and negative test scenarios. The test model is enriched with semantic information to distinguish valid from invalid values and value combinations. Unfortunately, it is easy to create over-constrained models and invalid values or invalid value combinations do not appear in the final test suite. In this paper, we extend previous work on manual repair and develop a technique to semi-automatically repair over-constrained models. The technique is evaluated with benchmark models and the results indicate a small computational overhead.