Hypervolume-based search for test case prioritization

Conference paper (2015)

Authors

Dario Di Nucci University of Salerno
A. Panichella Software Engineering - EEMCS
A.E. Zaidman Software Engineering - EEMCS
Andrea De Lucia University of Salerno
Department
Software Technology
Copyright: © 2015 Dario Di Nucci, A. Panichella, A.E. Zaidman, Andrea De Lucia
DOI
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https://doi.org/10.1007/978-3-319-22183-0_11
Genetic algorithm Hypervolume Test case prioritization
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Published Date

01-01-2015

Language

English

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Department

Software Technology

Abstract

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.