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Hypervolume-based search for test case prioritization. / Di Nucci, Dario; Panichella, Annibale; Zaidman, Andy; De Lucia, Andrea.

Search-Based Software Engineering - 7th International Symposium, SSBSE 2015, Proceedings. Vol. 9275 Springer, 2015. p. 157-172 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9275).

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Harvard

Di Nucci, D, Panichella, A, Zaidman, A & De Lucia, A 2015, Hypervolume-based search for test case prioritization. in Search-Based Software Engineering - 7th International Symposium, SSBSE 2015, Proceedings. vol. 9275, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9275, Springer, pp. 157-172, 7th International Symposium on Search-Based Software Engineering, SSBSE 2015, Bergamo, Italy, 5/09/15. https://doi.org/10.1007/978-3-319-22183-0_11

APA

Di Nucci, D., Panichella, A., Zaidman, A., & De Lucia, A. (2015). Hypervolume-based search for test case prioritization. In Search-Based Software Engineering - 7th International Symposium, SSBSE 2015, Proceedings (Vol. 9275, pp. 157-172). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9275). Springer. https://doi.org/10.1007/978-3-319-22183-0_11

Vancouver

Di Nucci D, Panichella A, Zaidman A, De Lucia A. Hypervolume-based search for test case prioritization. In Search-Based Software Engineering - 7th International Symposium, SSBSE 2015, Proceedings. Vol. 9275. Springer. 2015. p. 157-172. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-319-22183-0_11

Author

Di Nucci, Dario ; Panichella, Annibale ; Zaidman, Andy ; De Lucia, Andrea. / Hypervolume-based search for test case prioritization. Search-Based Software Engineering - 7th International Symposium, SSBSE 2015, Proceedings. Vol. 9275 Springer, 2015. pp. 157-172 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

BibTeX

@inproceedings{1303efd8033249cd94fc494b858b73f9,
title = "Hypervolume-based search for test case prioritization",
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.",
keywords = "Genetic algorithm, Hypervolume, Test case prioritization",
author = "{Di Nucci}, Dario and Annibale Panichella and Andy Zaidman and {De Lucia}, Andrea",
year = "2015",
month = "1",
day = "1",
doi = "10.1007/978-3-319-22183-0_11",
language = "English",
isbn = "9783319221823",
volume = "9275",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer",
pages = "157--172",
booktitle = "Search-Based Software Engineering - 7th International Symposium, SSBSE 2015, Proceedings",

}

RIS

TY - GEN

T1 - Hypervolume-based search for test case prioritization

AU - Di Nucci, Dario

AU - Panichella, Annibale

AU - Zaidman, Andy

AU - De Lucia, Andrea

PY - 2015/1/1

Y1 - 2015/1/1

N2 - 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.

AB - 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.

KW - Genetic algorithm

KW - Hypervolume

KW - Test case prioritization

UR - http://www.scopus.com/inward/record.url?scp=84951286528&partnerID=8YFLogxK

U2 - 10.1007/978-3-319-22183-0_11

DO - 10.1007/978-3-319-22183-0_11

M3 - Conference contribution

SN - 9783319221823

VL - 9275

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 157

EP - 172

BT - Search-Based Software Engineering - 7th International Symposium, SSBSE 2015, Proceedings

PB - Springer

ER -

ID: 47052768