Showing papers by "Hyunsook Do published in 2006"

On the Use of Mutation Faults in Empirical Assessments of Test Case Prioritization Techniques

Abstract: Regression testing is an important activity in the software life cycle, but it can also be very expensive. To reduce the cost of regression testing, software testers may prioritize their test cases so that those which are more important, by some measure, are run earlier in the regression testing process. One potential goal of test case prioritization techniques is to increase a test suite's rate of fault detection (how quickly, in a run of its test cases, that test suite can detect faults). 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 primarily to hand-seeded faults, largely 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 and that the use of hand-seeded faults can be problematic for the validity of empirical results focusing on fault detection. We have therefore designed and performed two controlled experiments assessing the ability of prioritization techniques to improve the rate of fault detection of test case prioritization 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. More importantly, a comparison of our results with those collected using hand-seeded faults reveals several implications for researchers performing empirical studies of test case prioritization techniques in particular and testing techniques in general

Prioritizing JUnit Test Cases: An Empirical Assessment and Cost-Benefits Analysis

Abstract: Test case prioritization provides a way to run test cases with the highest priority earliest. Numerous empirical studies have shown that prioritization can improve a test suite's rate of fault detection, but the extent to which these results generalize is an open question because the studies have all focused on a single procedural language, C, and a few specific types of test suites. In particular, Java and the JUnit testing framework are being used extensively to build software systems in practice, and the effectiveness of prioritization techniques on Java systems tested under JUnit has not been investigated. We have therefore designed and performed a controlled experiment examining whether test case prioritization can be effective on Java programs tested under JUnit, and comparing the results to those achieved in earlier studies. Our analyses show that test case prioritization can significantly improve the rate of fault detection of JUnit test suites, but also reveal differences with respect to previous studies that can be related to the language and testing paradigm. To investigate the practical implications of these results, we present a set of cost-benefits models for test case prioritization, and show how the effectiveness differences observed can result in savings in practice, but vary substantially with the cost factors associated with particular testing processes.

An empirical study of regression testing techniques incorporating context and lifetime factors and improved cost-benefit models

Abstract: Regression testing is an important but expensive activity, and a great deal of research on regression testing methodologies has been performed. In recent years, much of this research has emphasized empirical studies, including evaluations of the effectiveness and efficiency of regression testing techniques. To date, however, most studies have been limited in terms of their consideration of testing context and system lifetime, and have used cost-benefit models that omit important factors and render some types of comparisons between techniques impossible. These limitations can cause studies to improperly assess the costs and benefits of regression testing techniques in practical settings. In this paper, we provide improved cost-benefit models for use in assessing regression testing methodologies, that incorporate context and lifetime factors not considered in prior studies, and we use these models to compare several common methodologies. Our results show that the factors we consider (in particular, time constraints and incremental resource availability) can affect assessments of the relative benefits of regression testing techniques, and suggest that particular classes of techniques may compare differently across different types of test suites.