Search-Based Data-Flow Test Generation

Coverage criteria based on data-flow have long been discussed in the literature, yet to date they are still of surprising little practical relevance. This is in part because 1) manually writing a unit test for a data-flow aspect is more challenging than writing a unit test that simply covers a branch or statement, 2) there is a lack of tools to support data-flow testing, and 3) there is a lack of empirical evidence on how well data-flow testing scales in practice. To overcome these problems, we present 1) a search-based technique to automatically generate unit tests for data-flow criteria, 2) an implementation of this technique in the EVOSUITE test generation tool, and 3) a large empirical study applying this tool to the SF100 corpus of 100 open source Java projects. On average, the number of coverage objectives is three times as high as for branch coverage. However, the level of coverage achieved by EVOSUITE is comparable to other criteria, and the increase in size is only 15%, leading to higher mutation scores. These results counter the common assumption that data-flow testing does not scale, and should help to re-establish data-flow testing as a viable alternative in practice.