By Topic

Using coverage information to predict the cost-effectiveness of regression testing strategies

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Rosenblum, D.S. ; Dept. of Inf. & Comput. Sci., California Univ., Irvine, CA, USA ; Weyuker, E.J.

Selective regression testing strategies attempt to choose an appropriate subset of test cases from among a previously run test suite for a software system, based on information about the changes made to the system to create new versions. Although there has been a significant amount of research in recent years on the design of such strategies, there has been very little investigation of their cost-effectiveness. The paper presents some computationally efficient predictors of the cost-effectiveness of the two main classes of selective regression testing approaches. These predictors are computed from data about the coverage relationship between the system under test and its test suite. The paper then describes case studies in which these predictors were used to predict the cost-effectiveness of applying two different regression testing strategies to two software systems. In one case study, the TESTTUBE method selected an average of 88.1 percent of the available test cases in each version, while the predictor predicted that 87.3 percent of the test cases would be selected on average

Published in:

Software Engineering, IEEE Transactions on  (Volume:23 ,  Issue: 3 )