Techniques and Tools for the Automatic Generation of Optimal Test Data at Code, Model and Interface Level

摘要

This article presents two different tools automating the generation of optimized test data for unit, model-based and integration testing by maximizing the coverage and minimizing the number of test cases required. To cope with these conflicting goals, hybrid self-adaptive and multi-objective evolutionary algorithms were applied. The efficiency was demonstrated by evaluating fault detection capability by mutation testing. Thanks to the effort reduction offered, the approach is particularly suitable for the verification of complex, safety-relevant software systems.