%0 Conference Paper %B International Symposium on Software Testing and Analysis (ISSTA'15) %D 2015 %T Empirical Evaluation of Pareto Efficient Multi-objective Regression Test Case Prioritisation %A Michael G. Epitropakis %A Shin Yoo %A Mark Harman %A Edmund K. Burke %K additional greedy algorithm %K coverage compaction %K multi-objective evolutionary algo- rithm %K Test case prioritization %X 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. %B International Symposium on Software Testing and Analysis (ISSTA'15) %I ACM %C Baltimore, MD, USA %G eng %0 Report %D 2014 %T Pareto Efficient Multi-Objective Regression Test Suite Prioritisation %A Michael G. Epitropakis %A Shin Yoo %A Mark Harman %A Edmund K. Burke %X Test suite prioritisation seeks a test case ordering 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. We study multi objective test suite prioritisation, evaluating it on multiple versions of five widely-used benchmark programs and a much larger real world system of over 1MLoC. Our multi objective algorithms find faults significantly faster and with large effect size for 20 of the 22 versions. We also introduce a non-lossy coverage compact 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. %I Department of Computer Science, University College London %C Gower Street, London %P 1--16 %8 04/2014 %G eng