Test Case Prioritization: A Family of Empirical Studies

Authors

Sebastian Elbaum, University of Nebraska-LincolnFollow
Alexey G. Malishevsky, Oregon State University
Gregg Rothermel, University of Nebraska-LincolnFollow

Date of this Version

2-2002

Comments

Published in IEEE TRANSACTIONS ON SOFTWARE ENGINEERING, VOL. 28, NO. 2, FEBRUARY 2002. Copyright © 2002 IEEE. Used by permission.

Abstract

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 results of studies that showed that prioritization techniques can significantly improve rate of fault detection. Those studies, however, raised several additional questions: 1) Can prioritization techniques be effective when targeted at specific modified versions; 2) what trade-offs exist between fine granularity and coarse granularity prioritization techniques; 3) can the incorporation of measures of fault proneness into prioritization techniques improve their effectiveness? 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. Fine-granularity 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 coarse-granularity techniques’ ability to improve rate of fault detection. Incorporation of fault-proneness techniques produced relatively small improvements over other techniques in terms of rate of fault detection, a result which ran contrary to our expectations. Our studies also show that the relative effectiveness of various techniques can vary significantly across target programs. Furthermore, our analysis shows that whether the effectiveness differences 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 beneficial.