Test case prioritization (TCP) is aimed at finding an ideal ordering for executing the available test cases to reveal faults earlier. To solve this problem greedy algorithms and meta-heuristics have been widely investigated, but in most cases there is no statistically significant difference between them in terms of effectiveness. The fitness function used to guide meta-heuristics condenses the cumulative coverage scores achieved by a test case ordering using the Area Under Curve (AUC) metric. In this paper we notice that the AUC metric represents a simplified version of the hypervolume metric used in many objective optimization and we propose HGA, a Hypervolume-based Genetic Algorithm, to solve the TCP problem when using multiple test criteria. The results shows that HGA is more cost-effective than the additional greedy algorithm on large systems and on average requires 36% of the execution time required by the additional greedy algorithm.

Original languageEnglish
Title of host publicationSearch-Based Software Engineering - 7th International Symposium, SSBSE 2015, Proceedings
Number of pages16
ISBN (Print)9783319221823
Publication statusPublished - 1 Jan 2015
Event7th International Symposium on Search-Based Software Engineering, SSBSE 2015 - Bergamo, Italy
Duration: 5 Sep 20157 Sep 2015

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference7th International Symposium on Search-Based Software Engineering, SSBSE 2015

    Research areas

  • Genetic algorithm, Hypervolume, Test case prioritization

ID: 47052768