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 such prioritization is to increase a test suite's rate of fault detection. Previous work reported the results of studies that showed that prioritization techniques can signi cantly improve rate of fault detection. Those studies, however, raised several additional questions: 1 can prioritization techniques be e ective when targeted at speci c modi ed versions; 2 what tradeo s exist between ne granularity and coarse granularity prioritization techniques; 3 can the incorporation of measures of fault proneness into prioritization techniques improve their e ectiveness? To address these questions, we have performed several new studies, in which we empirically compared prioritization techniques using both controlled experiments and case studies. The results of these studies show that each of the prioritization techniques considered can improve the rate of fault detection of test suites overall. Finegranularity techniques typically outperformed coarse-granularity techniques, but only by a relatively small margin overall; in other words, the relative imprecision in coarse-granularity analysis did not dramatically reduce their ability to improve rate of fault detection. Incorporation of fault-proneness techniques produced relatively small improvements over other techniques in terms rate of fault detection, a result which ran contrary to our expectations. Our studies also show that the relative e ectiveness of various techniques can vary signi cantly across target programs. Furthermore, our analysis shows that whether the e ectiveness di erences observed will result in savings in practice varies substantially with the cost factors associated with particular testing processes. Further work to understand the sources of this variance, and to incorporate such understanding into prioritization techniques and the choice of techniques, would be bene cial.

Test case prioritization techniques schedule test cases for execution in an order that attempts to increase their effectiveness at meeting some performance goal. Various goals are possible; one involves rate of fault detection -a measure of how quickly faults are detected within the testing process. An improved rate of fault detection during testing can provide faster feedback on the system under test and let software engineers begin correcting faults earlier than might otherwise be possible. One application of prioritization techniques involves regression testing -the retesting of software following modifications; in this context, prioritization techniques can take advantage of information gathered about the previous execution of test cases to obtain test case orderings. In this paper, we describe several techniques for using test execution information to prioritize test cases for regression testing, including: (1) techniques that order test cases based on their total coverage of code components; (2) techniques that order test cases based on their coverage of code components not previously covered; (3) techniques that order test cases based on their estimated ability to reveal faults in the code components that they cover. We report the results of several experiments in which we applied these techniques to various test suites for various programs and measured the rates of fault detection achieved by the prioritized test suites, comparing those rates to the rates achieved by untreated, randomly ordered, and optimally ordered suites. Analysis of the data shows that each of the prioritization techniques studied improved the rate of fault detection of test suites, and this improvement occurred even with the least expensive of those techniques. The data also shows, however, that considerable room remains for improvement. The studies highlight several cost-benefits tradeoffs among the techniques studied, as well as several opportunities for future work.

Regression testing is an expensive testing process used to validate modified software and detect whether new faults have been introduced into previously tested code. 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 goal of prioritization is to increase a test suite's rate of fault detection. Previous empirical studies have shown that several prioritization techniques can significantly improve rate of fault detection, but these studies have also shown that the effectiveness of these techniques varies considerably across various attributes of the program, test suites, and modifications being considered. This variation makes it difficult for a practitioner to choose an appropriate prioritization technique for a given testing scenario. To address this problem, we analyze the fault detection rates that result from applying several different prioritization techniques to several programs and modified versions. The results of our analyses provide insights into what types of prioritization techniques are and are not appropriate under specific testing scenarios, and the conditions under which they are or are not appropriate. Our analysis approach can also be used by other researchers or practitioners to determine the prioritization techniques appropriate to other workloads.

Test case prioritization techniques schedule test cases for execution in an order that attempts to increase their effectiveness at meeting some performance goal. Various goals are possible; one involves rate of fault detection -a measure of how quickly faults are detected within the testing process. An improved rate of fault detection during testing can provide faster feedback on the system under test and let software engineers begin correcting faults earlier than might otherwise be possible. One application of prioritization techniques involves regression testing -the retesting of software following modifications; in this context, prioritization techniques can take advantage of information gathered about the previous execution of test cases to obtain test case orderings. In this paper, we describe several techniques for using test execution information to prioritize test cases for regression testing, including: (1) techniques that order test cases based on their total coverage of code components; (2) techniques that order test cases based on their coverage of code components not previously covered; (3) techniques that order test cases based on their estimated ability to reveal faults in the code components that they cover. We report the results of several experiments in which we applied these techniques to various test suites for various programs and measured the rates of fault detection achieved by the prioritized test suites, comparing those rates to the rates achieved by untreated, randomly ordered, and optimally ordered suites. Analysis of the data shows that each of the prioritization techniques studied improved the rate of fault detection of test suites, and this improvement occurred even with the least expensive of those techniques. The data also shows, however, that considerable room remains for improvement. The studies highlight several cost-benefits tradeoffs among the techniques studied, as well as several opportunities for future work.

Regression testing is an expensive testing process used to re-validate software as it evolves. Various methodologies for improving regression testing processes have been explored, but the cost-effectiveness of these methodologies has been shown to vary with characteristics of regression test suites. One such characteristic involves the way in which test inputs are composed into test cases within a test suite. This article reports the results of controlled experiments examining the effects of two factors in test suite composition -test suite granularity and test input grouping -on the costs and benefits of several regression-testing-related methodologies: retest-all, regression test selection, test suite reduction, and test case prioritization. These experiments consider the application of several specific techniques, from each of these methodologies, across ten releases each of two substantial software systems, using seven levels of test suite granularity and two types of test input grouping. The effects of granularity, technique, and grouping on the cost and fault-detection effectiveness of regression testing under the given methodologies are analyzed. This analysis shows that test suite granularity significantly affects several cost-benefit factors for the methodologies considered, while test input grouping has limited effects. Further, the results expose essential tradeoffs affecting the relationship between test suite design and regression testing cost-effectiveness, with several implications for practice.