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

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.

Keywords
additional greedy algorithm; coverage compaction; multi-objective evolutionary algorithm; Test case prioritization