Multi-objective Test Case Prioritization Using Improved Pareto-Optimal Clonal Selection Algorithm

Abstract Regression test plays a vital role in software testing by ensuring the quality and stability of the developed software. During regression test a large number of test cases are involved thus making the process expensive and difficult. In order to reduce the cost and time of regression test,...
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Autor*in:	Tulasiraman, Megala [verfasserIn] Vivekanandan, Nivethitha Kalimuthu, Vivekanandan

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2018

Schlagwörter:	Pareto-optimal Multi-objective test case prioritization Clonal selection algorithm

Anmerkung:	© 3D Research Center, Kwangwoon University and Springer-Verlag GmbH Germany, part of Springer Nature 2018

Übergeordnetes Werk:	Enthalten in: 3D Research - Berlin : Springer, 2010, 9(2018), 3 vom: 23. Juli
Übergeordnetes Werk:	volume:9 ; year:2018 ; number:3 ; day:23 ; month:07

Links:	Volltext

DOI / URN:	10.1007/s13319-018-0182-y

Katalog-ID:	SPR031330231

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520			\|a Abstract Regression test plays a vital role in software testing by ensuring the quality and stability of the developed software. During regression test a large number of test cases are involved thus making the process expensive and difficult. In order to reduce the cost and time of regression test, test case prioritization is applied. However in real world scenario multiple testing criteria and constraint are evolved, such as to detect all faults within minimum time, and to detect most severe faults earlier. This takes the test case prioritization problem turn into multi-objective test case prioritization paradigm. In this paper an improved pareto-optimal clonal selection algorithm is proposed to generate test case order depending on three objective such as minimum execution time, maximum severity fault identification and cost-cognizant average percentage of fault detected. The experimental analysis is conducted over an industrial project with seven different versions for which the proposed approach generates scheduled test case order. And it is concluded that the performance of proposed approach is better than other tested algorithms like random approach, weighted genetic algorithm, greedy and NSGA-II. Graphical Abstract
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allfields	10.1007/s13319-018-0182-y doi (DE-627)SPR031330231 (SPR)s13319-018-0182-y-e DE-627 ger DE-627 rakwb eng Tulasiraman, Megala verfasserin aut Multi-objective Test Case Prioritization Using Improved Pareto-Optimal Clonal Selection Algorithm 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © 3D Research Center, Kwangwoon University and Springer-Verlag GmbH Germany, part of Springer Nature 2018 Abstract Regression test plays a vital role in software testing by ensuring the quality and stability of the developed software. During regression test a large number of test cases are involved thus making the process expensive and difficult. In order to reduce the cost and time of regression test, test case prioritization is applied. However in real world scenario multiple testing criteria and constraint are evolved, such as to detect all faults within minimum time, and to detect most severe faults earlier. This takes the test case prioritization problem turn into multi-objective test case prioritization paradigm. In this paper an improved pareto-optimal clonal selection algorithm is proposed to generate test case order depending on three objective such as minimum execution time, maximum severity fault identification and cost-cognizant average percentage of fault detected. The experimental analysis is conducted over an industrial project with seven different versions for which the proposed approach generates scheduled test case order. And it is concluded that the performance of proposed approach is better than other tested algorithms like random approach, weighted genetic algorithm, greedy and NSGA-II. Graphical Abstract Pareto-optimal (dpeaa)DE-He213 Multi-objective test case prioritization (dpeaa)DE-He213 Clonal selection algorithm (dpeaa)DE-He213 Vivekanandan, Nivethitha aut Kalimuthu, Vivekanandan aut Enthalten in 3D Research Berlin : Springer, 2010 9(2018), 3 vom: 23. Juli (DE-627)624823733 (DE-600)2550008-9 2092-6731 nnns volume:9 year:2018 number:3 day:23 month:07 https://dx.doi.org/10.1007/s13319-018-0182-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_120 GBV_ILN_266 GBV_ILN_281 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2055 GBV_ILN_2059 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 AR 9 2018 3 23 07
spelling	10.1007/s13319-018-0182-y doi (DE-627)SPR031330231 (SPR)s13319-018-0182-y-e DE-627 ger DE-627 rakwb eng Tulasiraman, Megala verfasserin aut Multi-objective Test Case Prioritization Using Improved Pareto-Optimal Clonal Selection Algorithm 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © 3D Research Center, Kwangwoon University and Springer-Verlag GmbH Germany, part of Springer Nature 2018 Abstract Regression test plays a vital role in software testing by ensuring the quality and stability of the developed software. During regression test a large number of test cases are involved thus making the process expensive and difficult. In order to reduce the cost and time of regression test, test case prioritization is applied. However in real world scenario multiple testing criteria and constraint are evolved, such as to detect all faults within minimum time, and to detect most severe faults earlier. This takes the test case prioritization problem turn into multi-objective test case prioritization paradigm. In this paper an improved pareto-optimal clonal selection algorithm is proposed to generate test case order depending on three objective such as minimum execution time, maximum severity fault identification and cost-cognizant average percentage of fault detected. The experimental analysis is conducted over an industrial project with seven different versions for which the proposed approach generates scheduled test case order. And it is concluded that the performance of proposed approach is better than other tested algorithms like random approach, weighted genetic algorithm, greedy and NSGA-II. Graphical Abstract Pareto-optimal (dpeaa)DE-He213 Multi-objective test case prioritization (dpeaa)DE-He213 Clonal selection algorithm (dpeaa)DE-He213 Vivekanandan, Nivethitha aut Kalimuthu, Vivekanandan aut Enthalten in 3D Research Berlin : Springer, 2010 9(2018), 3 vom: 23. Juli (DE-627)624823733 (DE-600)2550008-9 2092-6731 nnns volume:9 year:2018 number:3 day:23 month:07 https://dx.doi.org/10.1007/s13319-018-0182-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_120 GBV_ILN_266 GBV_ILN_281 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2055 GBV_ILN_2059 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 AR 9 2018 3 23 07
allfields_unstemmed	10.1007/s13319-018-0182-y doi (DE-627)SPR031330231 (SPR)s13319-018-0182-y-e DE-627 ger DE-627 rakwb eng Tulasiraman, Megala verfasserin aut Multi-objective Test Case Prioritization Using Improved Pareto-Optimal Clonal Selection Algorithm 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © 3D Research Center, Kwangwoon University and Springer-Verlag GmbH Germany, part of Springer Nature 2018 Abstract Regression test plays a vital role in software testing by ensuring the quality and stability of the developed software. During regression test a large number of test cases are involved thus making the process expensive and difficult. In order to reduce the cost and time of regression test, test case prioritization is applied. However in real world scenario multiple testing criteria and constraint are evolved, such as to detect all faults within minimum time, and to detect most severe faults earlier. This takes the test case prioritization problem turn into multi-objective test case prioritization paradigm. In this paper an improved pareto-optimal clonal selection algorithm is proposed to generate test case order depending on three objective such as minimum execution time, maximum severity fault identification and cost-cognizant average percentage of fault detected. The experimental analysis is conducted over an industrial project with seven different versions for which the proposed approach generates scheduled test case order. And it is concluded that the performance of proposed approach is better than other tested algorithms like random approach, weighted genetic algorithm, greedy and NSGA-II. Graphical Abstract Pareto-optimal (dpeaa)DE-He213 Multi-objective test case prioritization (dpeaa)DE-He213 Clonal selection algorithm (dpeaa)DE-He213 Vivekanandan, Nivethitha aut Kalimuthu, Vivekanandan aut Enthalten in 3D Research Berlin : Springer, 2010 9(2018), 3 vom: 23. Juli (DE-627)624823733 (DE-600)2550008-9 2092-6731 nnns volume:9 year:2018 number:3 day:23 month:07 https://dx.doi.org/10.1007/s13319-018-0182-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_120 GBV_ILN_266 GBV_ILN_281 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2055 GBV_ILN_2059 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 AR 9 2018 3 23 07
allfieldsGer	10.1007/s13319-018-0182-y doi (DE-627)SPR031330231 (SPR)s13319-018-0182-y-e DE-627 ger DE-627 rakwb eng Tulasiraman, Megala verfasserin aut Multi-objective Test Case Prioritization Using Improved Pareto-Optimal Clonal Selection Algorithm 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © 3D Research Center, Kwangwoon University and Springer-Verlag GmbH Germany, part of Springer Nature 2018 Abstract Regression test plays a vital role in software testing by ensuring the quality and stability of the developed software. During regression test a large number of test cases are involved thus making the process expensive and difficult. In order to reduce the cost and time of regression test, test case prioritization is applied. However in real world scenario multiple testing criteria and constraint are evolved, such as to detect all faults within minimum time, and to detect most severe faults earlier. This takes the test case prioritization problem turn into multi-objective test case prioritization paradigm. In this paper an improved pareto-optimal clonal selection algorithm is proposed to generate test case order depending on three objective such as minimum execution time, maximum severity fault identification and cost-cognizant average percentage of fault detected. The experimental analysis is conducted over an industrial project with seven different versions for which the proposed approach generates scheduled test case order. And it is concluded that the performance of proposed approach is better than other tested algorithms like random approach, weighted genetic algorithm, greedy and NSGA-II. Graphical Abstract Pareto-optimal (dpeaa)DE-He213 Multi-objective test case prioritization (dpeaa)DE-He213 Clonal selection algorithm (dpeaa)DE-He213 Vivekanandan, Nivethitha aut Kalimuthu, Vivekanandan aut Enthalten in 3D Research Berlin : Springer, 2010 9(2018), 3 vom: 23. Juli (DE-627)624823733 (DE-600)2550008-9 2092-6731 nnns volume:9 year:2018 number:3 day:23 month:07 https://dx.doi.org/10.1007/s13319-018-0182-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_120 GBV_ILN_266 GBV_ILN_281 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2055 GBV_ILN_2059 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 AR 9 2018 3 23 07
allfieldsSound	10.1007/s13319-018-0182-y doi (DE-627)SPR031330231 (SPR)s13319-018-0182-y-e DE-627 ger DE-627 rakwb eng Tulasiraman, Megala verfasserin aut Multi-objective Test Case Prioritization Using Improved Pareto-Optimal Clonal Selection Algorithm 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © 3D Research Center, Kwangwoon University and Springer-Verlag GmbH Germany, part of Springer Nature 2018 Abstract Regression test plays a vital role in software testing by ensuring the quality and stability of the developed software. During regression test a large number of test cases are involved thus making the process expensive and difficult. In order to reduce the cost and time of regression test, test case prioritization is applied. However in real world scenario multiple testing criteria and constraint are evolved, such as to detect all faults within minimum time, and to detect most severe faults earlier. This takes the test case prioritization problem turn into multi-objective test case prioritization paradigm. In this paper an improved pareto-optimal clonal selection algorithm is proposed to generate test case order depending on three objective such as minimum execution time, maximum severity fault identification and cost-cognizant average percentage of fault detected. The experimental analysis is conducted over an industrial project with seven different versions for which the proposed approach generates scheduled test case order. And it is concluded that the performance of proposed approach is better than other tested algorithms like random approach, weighted genetic algorithm, greedy and NSGA-II. Graphical Abstract Pareto-optimal (dpeaa)DE-He213 Multi-objective test case prioritization (dpeaa)DE-He213 Clonal selection algorithm (dpeaa)DE-He213 Vivekanandan, Nivethitha aut Kalimuthu, Vivekanandan aut Enthalten in 3D Research Berlin : Springer, 2010 9(2018), 3 vom: 23. Juli (DE-627)624823733 (DE-600)2550008-9 2092-6731 nnns volume:9 year:2018 number:3 day:23 month:07 https://dx.doi.org/10.1007/s13319-018-0182-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_120 GBV_ILN_266 GBV_ILN_281 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2055 GBV_ILN_2059 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 AR 9 2018 3 23 07
language	English
source	Enthalten in 3D Research 9(2018), 3 vom: 23. Juli volume:9 year:2018 number:3 day:23 month:07
sourceStr	Enthalten in 3D Research 9(2018), 3 vom: 23. Juli volume:9 year:2018 number:3 day:23 month:07
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Pareto-optimal Multi-objective test case prioritization Clonal selection algorithm
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container_title	3D Research
authorswithroles_txt_mv	Tulasiraman, Megala @@aut@@ Vivekanandan, Nivethitha @@aut@@ Kalimuthu, Vivekanandan @@aut@@
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author	Tulasiraman, Megala
spellingShingle	Tulasiraman, Megala misc Pareto-optimal misc Multi-objective test case prioritization misc Clonal selection algorithm Multi-objective Test Case Prioritization Using Improved Pareto-Optimal Clonal Selection Algorithm
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topic_title	Multi-objective Test Case Prioritization Using Improved Pareto-Optimal Clonal Selection Algorithm Pareto-optimal (dpeaa)DE-He213 Multi-objective test case prioritization (dpeaa)DE-He213 Clonal selection algorithm (dpeaa)DE-He213
topic	misc Pareto-optimal misc Multi-objective test case prioritization misc Clonal selection algorithm
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title	Multi-objective Test Case Prioritization Using Improved Pareto-Optimal Clonal Selection Algorithm
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title_full	Multi-objective Test Case Prioritization Using Improved Pareto-Optimal Clonal Selection Algorithm
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title_sort	multi-objective test case prioritization using improved pareto-optimal clonal selection algorithm
title_auth	Multi-objective Test Case Prioritization Using Improved Pareto-Optimal Clonal Selection Algorithm
abstract	Abstract Regression test plays a vital role in software testing by ensuring the quality and stability of the developed software. During regression test a large number of test cases are involved thus making the process expensive and difficult. In order to reduce the cost and time of regression test, test case prioritization is applied. However in real world scenario multiple testing criteria and constraint are evolved, such as to detect all faults within minimum time, and to detect most severe faults earlier. This takes the test case prioritization problem turn into multi-objective test case prioritization paradigm. In this paper an improved pareto-optimal clonal selection algorithm is proposed to generate test case order depending on three objective such as minimum execution time, maximum severity fault identification and cost-cognizant average percentage of fault detected. The experimental analysis is conducted over an industrial project with seven different versions for which the proposed approach generates scheduled test case order. And it is concluded that the performance of proposed approach is better than other tested algorithms like random approach, weighted genetic algorithm, greedy and NSGA-II. Graphical Abstract © 3D Research Center, Kwangwoon University and Springer-Verlag GmbH Germany, part of Springer Nature 2018
abstractGer	Abstract Regression test plays a vital role in software testing by ensuring the quality and stability of the developed software. During regression test a large number of test cases are involved thus making the process expensive and difficult. In order to reduce the cost and time of regression test, test case prioritization is applied. However in real world scenario multiple testing criteria and constraint are evolved, such as to detect all faults within minimum time, and to detect most severe faults earlier. This takes the test case prioritization problem turn into multi-objective test case prioritization paradigm. In this paper an improved pareto-optimal clonal selection algorithm is proposed to generate test case order depending on three objective such as minimum execution time, maximum severity fault identification and cost-cognizant average percentage of fault detected. The experimental analysis is conducted over an industrial project with seven different versions for which the proposed approach generates scheduled test case order. And it is concluded that the performance of proposed approach is better than other tested algorithms like random approach, weighted genetic algorithm, greedy and NSGA-II. Graphical Abstract © 3D Research Center, Kwangwoon University and Springer-Verlag GmbH Germany, part of Springer Nature 2018
abstract_unstemmed	Abstract Regression test plays a vital role in software testing by ensuring the quality and stability of the developed software. During regression test a large number of test cases are involved thus making the process expensive and difficult. In order to reduce the cost and time of regression test, test case prioritization is applied. However in real world scenario multiple testing criteria and constraint are evolved, such as to detect all faults within minimum time, and to detect most severe faults earlier. This takes the test case prioritization problem turn into multi-objective test case prioritization paradigm. In this paper an improved pareto-optimal clonal selection algorithm is proposed to generate test case order depending on three objective such as minimum execution time, maximum severity fault identification and cost-cognizant average percentage of fault detected. The experimental analysis is conducted over an industrial project with seven different versions for which the proposed approach generates scheduled test case order. And it is concluded that the performance of proposed approach is better than other tested algorithms like random approach, weighted genetic algorithm, greedy and NSGA-II. Graphical Abstract © 3D Research Center, Kwangwoon University and Springer-Verlag GmbH Germany, part of Springer Nature 2018
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title_short	Multi-objective Test Case Prioritization Using Improved Pareto-Optimal Clonal Selection Algorithm
url	https://dx.doi.org/10.1007/s13319-018-0182-y
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Multi-objective Test Case Prioritization Using Improved Pareto-Optimal Clonal Selection Algorithm

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