As new software-testing techniques are developed, before they can achieve widespread acceptance, their effectiveness at detecting defects must be evaluated. The most common way of evaluating testing techniques is with empirical studies, in which one or more techniques are tried out on software with known defects. However, the defects used can affect the performance of the techniques. To complicate matters, it is not even clear how to effectively describe or characterize defects. To address these problems, this article describes an experiment architecture for empirically evaluating testing techniques which takes both defect and test-suite characteristics into account. As proof of concept, an experiment on GUI-testing techniques is conducted. It provides evidence that the defect characteristics proposed do help explain defect detection, at least for GUI testing, and it explores the relationship between the coverage of defective code and the detection of defects.

Software-testing researchers have long sought recipes for test suites that detect faults well. In the literature, empirical studies of testing techniques abound, yet the ideal technique for detecting the desired kinds of faults in a given situation often remains unclear. This work shows how understanding the context in which testing occurs, in terms of factors likely to influence fault detection, can make evaluations of testing techniques more readily applicable to new situations. We present a methodology for discovering which factors do statistically affect fault detection, and we perform an experiment with a set of test-suite- and fault-related factors in the GUI testing of two fielded, open-source applications. Statement coverage and GUI-event coverage are found to be statistically related to the likelihood of detecting certain kinds of faults.