Selecting a Cost-Effective Test Case Prioritization Technique
Software Quality Control
Scaling regression testing to large software systems
Proceedings of the 12th ACM SIGSOFT twelfth international symposium on Foundations of software engineering
Empirical Software Engineering
Prioritizing JUnit Test Cases: An Empirical Assessment and Cost-Benefits Analysis
Empirical Software Engineering
Proceedings of the 14th ACM SIGSOFT international symposium on Foundations of software engineering
Using sensitivity analysis to create simplified economic models for regression testing
ISSTA '08 Proceedings of the 2008 international symposium on Software testing and analysis
An empirical study of the effect of time constraints on the cost-benefits of regression testing
Proceedings of the 16th ACM SIGSOFT International Symposium on Foundations of software engineering
Proceedings of the eighteenth international symposium on Software testing and analysis
A prioritization approach for software test cases based on Bayesian networks
FASE'07 Proceedings of the 10th international conference on Fundamental approaches to software engineering
Test coverage optimization for large code problems
Journal of Systems and Software
Using test cases as contract to ensure service compliance across releases
ICSOC'05 Proceedings of the Third international conference on Service-Oriented Computing
Regression testing minimization, selection and prioritization: a survey
Software Testing, Verification & Reliability
Development of a framework for test case prioritization using genetic algorithm
ACM SIGSOFT Software Engineering Notes
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Regression testing is an expensive activity that can accountfor a large proportion of the software maintenancebudget. Because engineers add tests into test suites as softwareevolves, over time, increased test suite size makesrevalidation of the software more expensive. Regression testselection, test suite reduction, and test case prioritizationtechniques can help with this, by reducing the number of regressiontests that must be run and by helping testers meettesting objectives more quickly. These techniques, however,can be expensive to employ and may not reduce overall regressiontesting costs. Thus, practitioners and researcherscould benefit from cost models that would help them assessthe cost-benefits of techniques. Cost models have beenproposed for this purpose, but some of these models omitimportant factors, and others cannot truly evaluate cost-effectiveness.In this paper, we present new cost-benefitsmodels for regression test selection, test suite reduction, andtest case prioritization, that capture previously omitted factors,and support cost-benefits analyses where they were notsupported before. We present the results of an empiricalstudy assessing these models.