A Hybrid Approach for Model-Based Random Testing

Random testing is a valuable supplement to systematic test methods because it discovers defects that are very hard to detect with systematic test strategies. We propose a novel approach for random test generation that combines the benefits of model-based testing, constraint satisfaction, and pure random testing. The proposed method has been incorporated into the IDATG (Integrating Design and Automated Test case Generation) tool-set and validated in a number of case studies. Their results indicate that using the new approach it is indeed possible to generate effective test cases in acceptable time.