Semi-automatic Search-Based Test Generation

Search-based testing techniques can efficiently generate test data to achieve high code coverage. However, when the fitness function does not provide sufficient guidance, the search will only generate optimal results by chance. Yet, where the search algorithm struggles, a human tester with domain knowledge can often produce solutions easily. We therefore include the tester in the test generation process: When the search stagnates, the tester is given an opportunity to improve the current solution, and these improvements are fed back to the search. In particular, relevant problems occur often when generating tests for object-oriented languages, where test cases are sequences of method calls. Constructing complex objects through sequences of method calls is difficult, and often the traditional branch distance offers little guidance - yet for a human tester the same task is often trivial. In this paper, we present a semi-automatic test generation approach based on our search-based EVO SUITE tool, and evaluate the usefulness and potential on a set of example classes.