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    Rights statement: © Authors, 2015. This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in ISSTA 2015 Proceedings of the 2015 International Symposium on Software Testing and Analysis http://dx.doi.org/10.1145/2771783.2771788

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Empirical evaluation of Pareto efficient multi-objective regression test case prioritisation

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSNConference contribution/Paperpeer-review

Published
Publication date13/07/2015
Host publicationISSTA 2015 Proceedings of the 2015 International Symposium on Software Testing and Analysis
Place of PublicationNew York
PublisherACM
Pages234-245
Number of pages12
ISBN (electronic)9781450336208
<mark>Original language</mark>English
Event24th International Symposium on Software Testing and Analysis, ISSTA 2015 - Baltimore, United States
Duration: 13/07/201517/07/2015

Conference

Conference24th International Symposium on Software Testing and Analysis, ISSTA 2015
Country/TerritoryUnited States
CityBaltimore
Period13/07/1517/07/15

Conference

Conference24th International Symposium on Software Testing and Analysis, ISSTA 2015
Country/TerritoryUnited States
CityBaltimore
Period13/07/1517/07/15

Abstract

The aim of test case prioritisation is to determine an ordering of test cases that maximises the likelihood of early fault revelation. Previous prioritisation techniques have tended to be single objective, for which the additional greedy algorithm is the current state-of-the-art. Unlike test suite minimisation, multi objective test case prioritisation has not been thoroughly evaluated. This paper presents an extensive empirical study of the effectiveness of multi objective test case prioritisation, evaluating it on multiple versions of five widely-used benchmark programs and a much larger real world system of over 1 million lines of code. The paper also presents a lossless coverage compaction algorithm that dramatically scales the performance of all algorithms studied by between 2 and 4 orders of magnitude, making prioritisation practical for even very demanding problems. Copyright is held by the owner/author(s).

Bibliographic note

© Authors, 2015. This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in ISSTA 2015 Proceedings of the 2015 International Symposium on Software Testing and Analysis http://dx.doi.org/10.1145/2771783.2771788