Using Defect Prediction to Improve the Bug Detection Capability of Search-Based Software Testing

TL;DR

This paper enhances search-based software testing by integrating defect prediction to focus test generation on likely buggy areas, improving bug detection efficiency especially under limited time constraints.

Contribution

It introduces two novel methods, SBST$_{CL}$ and SBST$_{ML}$, that incorporate defect prediction to guide test generation towards defect-prone code regions.

Findings

SBST$_{CL}$ outperforms existing SBST methods under tight time budgets

The approaches effectively target defect-prone areas, increasing bug detection

Empirical results on 434 real bugs demonstrate improved efficiency

Abstract

Automated test generators, such as search based software testing (SBST) techniques, replace the tedious and expensive task of manually writing test cases. SBST techniques are effective at generating tests with high code coverage. However, is high code coverage sufficient to maximise the number of bugs found? We argue that SBST needs to be focused to search for test cases in defective areas rather in non-defective areas of the code in order to maximise the likelihood of discovering the bugs. Defect prediction algorithms give useful information about the bug-prone areas in software. Therefore, we formulate the objective of this thesis: \textit{Improve the bug detection capability of SBST by incorporating defect prediction information}. To achieve this, we devise two research objectives, i.e., 1) Develop a novel approach (SBST$_{CL}$) that allocates time budget to classes based on the likelihood of classes being defective, and 2) Develop a novel strategy (SBST$_{ML}$) to guide the underlying search algorithm (i.e., genetic algorithm) towards the defective areas in a class. Through empirical evaluation on 434 real reported bugs in the Defects4J dataset, we demonstrate that our novel approach, SBST$_{CL}$, is significantly more efficient than the state of the art SBST when they are given a tight time budget in a resource constrained environment.