A Controlled Experiment Assessing Test Case Prioritization Techniques via Mutation Faults

Regression testing is an important part of software maintenance, but it can also be very expensive. To reduce this expense, software testers may prioritize their test cases so that those that are more important are run earlier in the regression testing process. Previous work has shown that prioritization can improve a test suite’s rate of fault detection, but the assessment of prioritization techniques has been limited to hand-seeded faults, primarily due to the belief that such faults are more realistic than automatically generated (mutation) faults. A recent empirical study, however, suggests that mutation faults can be representative of real faults. We have therefore designed and performed a controlled experiment to assess the ability of prioritization techniques to improve the rate of fault detection techniques, measured relative to mutation faults. Our results show that prioritization can be effective relative to the faults considered, and they expose ways in which that effectiveness can vary with characteristics of faults and test suites. We also compare our results to those collected earlier with respect to the relationship between hand-seeded faults and mutation faults, and the implications this has for researchers performing empirical studies of prioritization.

Incorporating Varying Test Costs and Fault Severities into Test Case Prioritization

Test case prioritization techniques schedule test cases for regression testing in an order that increases their ability to meet some performance goal. One performance goal, rate offault detection, measures how quickly faults are detected within the testing process. In previous work we provided a metric, APFD, for measuring rate of fault detection, and techniques for prioritizing test cases to improve APFD, and reported the results of experiments using those techniques. This metric and these techniques, however, applied only in cases in which test costs and fault severity are uniform. In this paper, we present a new metric for assessing the rate of fault detection of prioritized test cases, that incorporates varying test case and fault costs. We present the results of a case study illustrating the application of the metric. This study raises several practical questions that might arise in applying test case prioritization; we discuss how practitioners could go about answering these questions.