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Gregg Rothermel
Researcher at University of Nebraska–Lincoln
Publications - 196
Citations - 19018
Gregg Rothermel is an academic researcher from University of Nebraska–Lincoln. The author has contributed to research in topics: Regression testing & Test case. The author has an hindex of 66, co-authored 196 publications receiving 17893 citations. Previous affiliations of Gregg Rothermel include Samsung & Clemson University.
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Journal ArticleDOI
Prioritizing test cases for regression testing
TL;DR: Test case prioritization techniques schedule test cases for execution in an order that attempts to increase their effectiveness at meeting some performance goal as discussed by the authors, such as rate of fault detection, a measure of how quickly faults are detected within the testing process.
Journal ArticleDOI
Supporting Controlled Experimentation with Testing Techniques: An Infrastructure and its Potential Impact
TL;DR: The infrastructure that is being designed and constructed to support controlled experimentation with testing and regression testing techniques is described and the impact that this infrastructure has had and can be expected to have.
Journal ArticleDOI
Test case prioritization: a family of empirical studies
TL;DR: In this article, the authors empirically compared the effectiveness of fine granularity and coarse granularity prioritization techniques using both controlled experiments and case studies, and found that the incorporation of measures of fault proneness into prioritization technique improves their effectiveness.
Proceedings ArticleDOI
Prioritizing test cases for regression testing
TL;DR: Can prioritization techniques be effective when aimed at specific modified versions; what tradeoffs exist between fine granularity and coarse granularity prioritized techniques; and can the incorporation of measures of fault proneness into prioritization technique improve their effectiveness?
Journal ArticleDOI
A safe, efficient regression test selection technique
TL;DR: Initial empirical studies indicate that the technique can significantly reduce the cost of regression testing modified software and is at lease as precise as other safe regression test selection algorithms.