Defect Prediction Guided Search-Based Software Testing

TL;DR

This paper introduces a defect prediction guided test generation method that allocates more testing resources to likely defective code parts, improving bug detection efficiency in automated Java testing.

Contribution

It integrates defect prediction into search-based testing, enhancing bug-finding effectiveness without increasing testing budget.

Findings

Increased bug detection in defect-prone code regions

Improved testing efficiency with defect-guided approach

Validated on Defects4J benchmark

Abstract

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.