|Appears in Collections:||Computing Science and Mathematics Conference Papers and Proceedings|
|Peer Review Status:||Refereed|
|Title:||Empirical Evaluation of Pareto Efficient Multi-objective Regression Test Case Prioritisation|
|Citation:||Epitropakis M, Yoo S, Harman M & Burke E (2015) Empirical Evaluation of Pareto Efficient Multi-objective Regression Test Case Prioritisation. In: International Symposium on Software Testing and Analysis (ISSTA'15). International Symposium on Software Testing and Analysis (ISSTA'15), Baltimore, MD, USA, 12.07.2015-17.07.2015. New York, NY, USA: ACM, pp. 234-245. https://doi.org/10.1145/2771783.2771788|
|Conference Name:||International Symposium on Software Testing and Analysis (ISSTA'15)|
|Conference Dates:||2015-07-12 - 2015-07-17|
|Conference Location:||Baltimore, MD, USA|
|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.|
|Status:||VoR - Version of Record|
|Rights:||Copyright is held by author. Published in ISSTA’15 , July 13–17, 2015, Baltimore, MD, USA. ACM 978-1-4503-3620-8/15/07|
|it.pdf||Fulltext - Published Version||434.42 kB||Adobe PDF||View/Open|
This item is protected by original copyright
A file in this item is licensed under a Creative Commons License
Items in the Repository are protected by copyright, with all rights reserved, unless otherwise indicated.
If you believe that any material held in STORRE infringes copyright, please contact email@example.com providing details and we will remove the Work from public display in STORRE and investigate your claim.