How good does a Defect Predictor need to be to guide Search-Based Software Testing?

TL;DR

This study investigates how the accuracy of defect predictors, especially recall, influences the effectiveness of search-based software testing in bug detection, highlighting the importance of high recall for guiding testing efforts.

Contribution

It provides empirical evidence on the impact of defect predictor recall and precision on SBST bug detection, offering practical recommendations for defect predictor design.

Findings

Recall significantly affects bug detection effectiveness.

Precision has a negligible practical impact.

Increasing recall improves bug detection, especially when maintaining high precision.

Abstract

Defect predictors, static bug detectors and humans inspecting the code can locate the parts of the program that are buggy before they are discovered through testing. Automated test generators such as search-based software testing (SBST) techniques can use this information to direct their search for test cases to likely buggy code, thus speeding up the process of detecting existing bugs. However, often the predictions given by these tools or humans are imprecise, which can misguide the SBST technique and may deteriorate its performance. In this paper, we study the impact of imprecision in defect prediction on the bug detection effectiveness of SBST. Our study finds that the recall of the defect predictor, i.e., the probability of correctly identifying buggy code, has a significant impact on bug detection effectiveness of SBST with a large effect size. On the other hand, the effect of precision, a measure for false alarms, is not of meaningful practical significance as indicated by a very small effect size. In particular, the SBST technique finds 7.5 less bugs on average (out of 420 bugs) for every 5% decrements of the recall. In the context of combining defect prediction and SBST, our recommendation for practice is to increase the recall of defect predictors at the expense of precision, while maintaining a precision of at least 75%. To account for the imprecision of defect predictors, in particular low recall values, SBST techniques should be designed to search for test cases that also cover the predicted non-buggy parts of the program, while prioritising the parts that have been predicted as buggy.