Diversity-Driven Unit Test Generation (Data Set)

<p>The goal of automated unit test generation tools is to create a set of test cases for the software under test that achieve the highest possible coverage for the selected test quality criteria. The most&nbsp;effective approaches for achieving this goal at the present time use meta-heuristic optimization&nbsp;algorithms to search for new test cases using fitness functions defined on existing sets of test<br> cases and the system under test. Regardless of how their search algorithms are controlled, however, all existing approaches focus on the analysis of exactly one implementation, the software&nbsp;under test, to drive their search processes, which is a limitation on the information they have&nbsp;available. In this paper we investigate whether the practical effectiveness of white box unit test&nbsp;generation tools can be increased by giving them access to multiple, diverse implementations&nbsp;of the functionality under test harvested from widely available Open Source software repositories. After presenting a basic implementation of such an approach, DivGen (Diversity-driven&nbsp;Generation), on top of the leading test generation tool for Java (EvoSuite), we assess the performance of DivGen compared to EvoSuite when applied in its traditional, mono-implementation&nbsp;oriented mode (MonoGen). The results show that while DivGen outperforms MonoGen in 33%&nbsp;of the sampled classes for mutation coverage (+16% higher on average), MonoGen outperforms<br> DivGen in 12.4% of the classes for branch coverage (+10% higher average).</p>