Hypervolume-based search for test case prioritization

Dario Di Nucci*, Annibale Panichella, Andy Zaidman, Andrea De Lucia

*Corresponding author for this work

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

17 Citations (Scopus)
20 Downloads (Pure)


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


  • Genetic algorithm
  • Hypervolume
  • Test case prioritization


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