Defect Prediction Guided Search-Based Software Testing
This work addresses the challenge of maximizing bug detection under limited testing budgets for software developers, representing an incremental improvement over existing search-based software testing methods.
The paper tackles the problem of automated test generation by proposing a defect prediction-guided approach that allocates more testing budget to likely defective code parts, resulting in increased bug detection efficiency on the Defects4J benchmark.
Today, most automated test generators, such as search-based software testing (SBST) techniques focus on achieving high code coverage. However, high code coverage is not sufficient to maximise the number of bugs found, especially when given a limited testing budget. In this paper, we propose an automated test generation technique that is also guided by the estimated degree of defectiveness of the source code. Parts of the code that are likely to be more defective receive more testing budget than the less defective parts. To measure the degree of defectiveness, we leverage Schwa, a notable defect prediction technique. We implement our approach into EvoSuite, a state of the art SBST tool for Java. Our experiments on the Defects4J benchmark demonstrate the improved efficiency of defect prediction guided test generation and confirm our hypothesis that spending more time budget on likely defective parts increases the number of bugs found in the same time budget.