A feedback-directed method of evolutionary test data generation for parallel programs

Context: Genetic algorithms can be utilized for automatic test data generation. Test data are encoded as individuals which are evolved for a number of generations using genetic operators. Test data of a parallel program include not only the program input, but also the communication information betwe...
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Gespeichert in:

Autor*in:	Gong, Dunwei [verfasserIn] Pan, Feng [verfasserIn] Tian, Tian [verfasserIn] Yang, Su [verfasserIn] Meng, Fanlin [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2020

Schlagwörter:	Information utilization Genetic algorithm Parallel program Path coverage Test data

Übergeordnetes Werk:	Enthalten in: Information & software technology - Amsterdam [u.a.] : Elsevier Science, 1987, 124
Übergeordnetes Werk:	volume:124

DOI / URN:	10.1016/j.infsof.2020.106318

Katalog-ID:	ELV004105389

Internformat


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245	1	0	\|a A feedback-directed method of evolutionary test data generation for parallel programs
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337			\|a Computermedien \|b c \|2 rdamedia
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520			\|a Context: Genetic algorithms can be utilized for automatic test data generation. Test data are encoded as individuals which are evolved for a number of generations using genetic operators. Test data of a parallel program include not only the program input, but also the communication information between each pair of processes. Traditional genetic algorithms, however, do not make full use of information provided by a population’s evolution, resulting in a low efficiency in generating test data. Objective: This paper emphasizes the problem of test data generation for parallel programs, and presents a feedback-directed genetic algorithm for generating test data of path coverage. Method: Information related to a schedule sequence is exploited to improve genetic operators. Specifically, a scheduling sequence is evaluated according to how well an individual covers the target path. The probability of the crossover and mutation points being located in the region is determined based on the evaluation result, which prevents a good schedule sequence from being destroyed. If crossover and mutation are performed in the scheduling sequence, the location of crossover and mutation points is further determined according to the relationship between nodes to be covered and the scheduling sequence. In this way, the population can be evolved in a narrowed search space. Results: The proposed algorithm is applied to test 11 parallel programs. The experimental results show that, compared with the genetic algorithm without utilizing information during the population evolution, the proposed algorithm significantly reduces the number of generations and the time consumption. Conclusion: The proposed algorithm can greatly improve the efficiency in evolutionary test data generation.
650		4	\|a Information utilization
650		4	\|a Genetic algorithm
650		4	\|a Parallel program
650		4	\|a Path coverage
650		4	\|a Test data
700	1		\|a Pan, Feng \|e verfasserin \|4 aut
700	1		\|a Tian, Tian \|e verfasserin \|4 aut
700	1		\|a Yang, Su \|e verfasserin \|4 aut
700	1		\|a Meng, Fanlin \|e verfasserin \|0 (orcid)0000-0002-4866-0011 \|4 aut
773	0	8	\|i Enthalten in \|t Information & software technology \|d Amsterdam [u.a.] : Elsevier Science, 1987 \|g 124 \|h Online-Ressource \|w (DE-627)320419185 \|w (DE-600)2002332-7 \|w (DE-576)259271160 \|7 nnns
773	1	8	\|g volume:124
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912			\|a GBV_ILN_65
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912			\|a GBV_ILN_73
912			\|a GBV_ILN_74
912			\|a GBV_ILN_90
912			\|a GBV_ILN_95
912			\|a GBV_ILN_100
912			\|a GBV_ILN_101
912			\|a GBV_ILN_105
912			\|a GBV_ILN_110
912			\|a GBV_ILN_150
912			\|a GBV_ILN_151
912			\|a GBV_ILN_224
912			\|a GBV_ILN_370
912			\|a GBV_ILN_602
912			\|a GBV_ILN_702
912			\|a GBV_ILN_2003
912			\|a GBV_ILN_2004
912			\|a GBV_ILN_2005
912			\|a GBV_ILN_2006
912			\|a GBV_ILN_2008
912			\|a GBV_ILN_2010
912			\|a GBV_ILN_2011
912			\|a GBV_ILN_2014
912			\|a GBV_ILN_2015
912			\|a GBV_ILN_2020
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912			\|a GBV_ILN_2025
912			\|a GBV_ILN_2027
912			\|a GBV_ILN_2034
912			\|a GBV_ILN_2038
912			\|a GBV_ILN_2044
912			\|a GBV_ILN_2048
912			\|a GBV_ILN_2049
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912			\|a GBV_ILN_2059
912			\|a GBV_ILN_2061
912			\|a GBV_ILN_2064
912			\|a GBV_ILN_2065
912			\|a GBV_ILN_2068
912			\|a GBV_ILN_2088
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912			\|a GBV_ILN_2113
912			\|a GBV_ILN_2118
912			\|a GBV_ILN_2122
912			\|a GBV_ILN_2129
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912			\|a GBV_ILN_2336
912			\|a GBV_ILN_2470
912			\|a GBV_ILN_2507
912			\|a GBV_ILN_2522
912			\|a GBV_ILN_4035
912			\|a GBV_ILN_4037
912			\|a GBV_ILN_4046
912			\|a GBV_ILN_4112
912			\|a GBV_ILN_4125
912			\|a GBV_ILN_4126
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912			\|a GBV_ILN_4251
912			\|a GBV_ILN_4305
912			\|a GBV_ILN_4313
912			\|a GBV_ILN_4323
912			\|a GBV_ILN_4324
912			\|a GBV_ILN_4325
912			\|a GBV_ILN_4326
912			\|a GBV_ILN_4333
912			\|a GBV_ILN_4334
912			\|a GBV_ILN_4335
912			\|a GBV_ILN_4338
912			\|a GBV_ILN_4393
936	b	k	\|a 85.00 \|j Betriebswirtschaft: Allgemeines
936	b	k	\|a 54.50 \|j Programmierung: Allgemeines
951			\|a AR
952			\|d 124

Indexfelder

author_variant	d g dg f p fp t t tt s y sy f m fm
matchkey_str	gongdunweipanfengtiantianyangsumengfanli:2020----:febcdrcemtooeouinrtsdtgnrto
hierarchy_sort_str	2020
bklnumber	85.00 54.50
publishDate	2020
allfields	10.1016/j.infsof.2020.106318 doi (DE-627)ELV004105389 (ELSEVIER)S0950-5849(20)30070-7 DE-627 ger DE-627 rda eng 330 004 DE-600 85.00 bkl 54.50 bkl Gong, Dunwei verfasserin aut A feedback-directed method of evolutionary test data generation for parallel programs 2020 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Context: Genetic algorithms can be utilized for automatic test data generation. Test data are encoded as individuals which are evolved for a number of generations using genetic operators. Test data of a parallel program include not only the program input, but also the communication information between each pair of processes. Traditional genetic algorithms, however, do not make full use of information provided by a population’s evolution, resulting in a low efficiency in generating test data. Objective: This paper emphasizes the problem of test data generation for parallel programs, and presents a feedback-directed genetic algorithm for generating test data of path coverage. Method: Information related to a schedule sequence is exploited to improve genetic operators. Specifically, a scheduling sequence is evaluated according to how well an individual covers the target path. The probability of the crossover and mutation points being located in the region is determined based on the evaluation result, which prevents a good schedule sequence from being destroyed. If crossover and mutation are performed in the scheduling sequence, the location of crossover and mutation points is further determined according to the relationship between nodes to be covered and the scheduling sequence. In this way, the population can be evolved in a narrowed search space. Results: The proposed algorithm is applied to test 11 parallel programs. The experimental results show that, compared with the genetic algorithm without utilizing information during the population evolution, the proposed algorithm significantly reduces the number of generations and the time consumption. Conclusion: The proposed algorithm can greatly improve the efficiency in evolutionary test data generation. Information utilization Genetic algorithm Parallel program Path coverage Test data Pan, Feng verfasserin aut Tian, Tian verfasserin aut Yang, Su verfasserin aut Meng, Fanlin verfasserin (orcid)0000-0002-4866-0011 aut Enthalten in Information & software technology Amsterdam [u.a.] : Elsevier Science, 1987 124 Online-Ressource (DE-627)320419185 (DE-600)2002332-7 (DE-576)259271160 nnns volume:124 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 85.00 Betriebswirtschaft: Allgemeines 54.50 Programmierung: Allgemeines AR 124
spelling	10.1016/j.infsof.2020.106318 doi (DE-627)ELV004105389 (ELSEVIER)S0950-5849(20)30070-7 DE-627 ger DE-627 rda eng 330 004 DE-600 85.00 bkl 54.50 bkl Gong, Dunwei verfasserin aut A feedback-directed method of evolutionary test data generation for parallel programs 2020 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Context: Genetic algorithms can be utilized for automatic test data generation. Test data are encoded as individuals which are evolved for a number of generations using genetic operators. Test data of a parallel program include not only the program input, but also the communication information between each pair of processes. Traditional genetic algorithms, however, do not make full use of information provided by a population’s evolution, resulting in a low efficiency in generating test data. Objective: This paper emphasizes the problem of test data generation for parallel programs, and presents a feedback-directed genetic algorithm for generating test data of path coverage. Method: Information related to a schedule sequence is exploited to improve genetic operators. Specifically, a scheduling sequence is evaluated according to how well an individual covers the target path. The probability of the crossover and mutation points being located in the region is determined based on the evaluation result, which prevents a good schedule sequence from being destroyed. If crossover and mutation are performed in the scheduling sequence, the location of crossover and mutation points is further determined according to the relationship between nodes to be covered and the scheduling sequence. In this way, the population can be evolved in a narrowed search space. Results: The proposed algorithm is applied to test 11 parallel programs. The experimental results show that, compared with the genetic algorithm without utilizing information during the population evolution, the proposed algorithm significantly reduces the number of generations and the time consumption. Conclusion: The proposed algorithm can greatly improve the efficiency in evolutionary test data generation. Information utilization Genetic algorithm Parallel program Path coverage Test data Pan, Feng verfasserin aut Tian, Tian verfasserin aut Yang, Su verfasserin aut Meng, Fanlin verfasserin (orcid)0000-0002-4866-0011 aut Enthalten in Information & software technology Amsterdam [u.a.] : Elsevier Science, 1987 124 Online-Ressource (DE-627)320419185 (DE-600)2002332-7 (DE-576)259271160 nnns volume:124 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 85.00 Betriebswirtschaft: Allgemeines 54.50 Programmierung: Allgemeines AR 124
allfields_unstemmed	10.1016/j.infsof.2020.106318 doi (DE-627)ELV004105389 (ELSEVIER)S0950-5849(20)30070-7 DE-627 ger DE-627 rda eng 330 004 DE-600 85.00 bkl 54.50 bkl Gong, Dunwei verfasserin aut A feedback-directed method of evolutionary test data generation for parallel programs 2020 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Context: Genetic algorithms can be utilized for automatic test data generation. Test data are encoded as individuals which are evolved for a number of generations using genetic operators. Test data of a parallel program include not only the program input, but also the communication information between each pair of processes. Traditional genetic algorithms, however, do not make full use of information provided by a population’s evolution, resulting in a low efficiency in generating test data. Objective: This paper emphasizes the problem of test data generation for parallel programs, and presents a feedback-directed genetic algorithm for generating test data of path coverage. Method: Information related to a schedule sequence is exploited to improve genetic operators. Specifically, a scheduling sequence is evaluated according to how well an individual covers the target path. The probability of the crossover and mutation points being located in the region is determined based on the evaluation result, which prevents a good schedule sequence from being destroyed. If crossover and mutation are performed in the scheduling sequence, the location of crossover and mutation points is further determined according to the relationship between nodes to be covered and the scheduling sequence. In this way, the population can be evolved in a narrowed search space. Results: The proposed algorithm is applied to test 11 parallel programs. The experimental results show that, compared with the genetic algorithm without utilizing information during the population evolution, the proposed algorithm significantly reduces the number of generations and the time consumption. Conclusion: The proposed algorithm can greatly improve the efficiency in evolutionary test data generation. Information utilization Genetic algorithm Parallel program Path coverage Test data Pan, Feng verfasserin aut Tian, Tian verfasserin aut Yang, Su verfasserin aut Meng, Fanlin verfasserin (orcid)0000-0002-4866-0011 aut Enthalten in Information & software technology Amsterdam [u.a.] : Elsevier Science, 1987 124 Online-Ressource (DE-627)320419185 (DE-600)2002332-7 (DE-576)259271160 nnns volume:124 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 85.00 Betriebswirtschaft: Allgemeines 54.50 Programmierung: Allgemeines AR 124
allfieldsGer	10.1016/j.infsof.2020.106318 doi (DE-627)ELV004105389 (ELSEVIER)S0950-5849(20)30070-7 DE-627 ger DE-627 rda eng 330 004 DE-600 85.00 bkl 54.50 bkl Gong, Dunwei verfasserin aut A feedback-directed method of evolutionary test data generation for parallel programs 2020 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Context: Genetic algorithms can be utilized for automatic test data generation. Test data are encoded as individuals which are evolved for a number of generations using genetic operators. Test data of a parallel program include not only the program input, but also the communication information between each pair of processes. Traditional genetic algorithms, however, do not make full use of information provided by a population’s evolution, resulting in a low efficiency in generating test data. Objective: This paper emphasizes the problem of test data generation for parallel programs, and presents a feedback-directed genetic algorithm for generating test data of path coverage. Method: Information related to a schedule sequence is exploited to improve genetic operators. Specifically, a scheduling sequence is evaluated according to how well an individual covers the target path. The probability of the crossover and mutation points being located in the region is determined based on the evaluation result, which prevents a good schedule sequence from being destroyed. If crossover and mutation are performed in the scheduling sequence, the location of crossover and mutation points is further determined according to the relationship between nodes to be covered and the scheduling sequence. In this way, the population can be evolved in a narrowed search space. Results: The proposed algorithm is applied to test 11 parallel programs. The experimental results show that, compared with the genetic algorithm without utilizing information during the population evolution, the proposed algorithm significantly reduces the number of generations and the time consumption. Conclusion: The proposed algorithm can greatly improve the efficiency in evolutionary test data generation. Information utilization Genetic algorithm Parallel program Path coverage Test data Pan, Feng verfasserin aut Tian, Tian verfasserin aut Yang, Su verfasserin aut Meng, Fanlin verfasserin (orcid)0000-0002-4866-0011 aut Enthalten in Information & software technology Amsterdam [u.a.] : Elsevier Science, 1987 124 Online-Ressource (DE-627)320419185 (DE-600)2002332-7 (DE-576)259271160 nnns volume:124 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 85.00 Betriebswirtschaft: Allgemeines 54.50 Programmierung: Allgemeines AR 124
allfieldsSound	10.1016/j.infsof.2020.106318 doi (DE-627)ELV004105389 (ELSEVIER)S0950-5849(20)30070-7 DE-627 ger DE-627 rda eng 330 004 DE-600 85.00 bkl 54.50 bkl Gong, Dunwei verfasserin aut A feedback-directed method of evolutionary test data generation for parallel programs 2020 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Context: Genetic algorithms can be utilized for automatic test data generation. Test data are encoded as individuals which are evolved for a number of generations using genetic operators. Test data of a parallel program include not only the program input, but also the communication information between each pair of processes. Traditional genetic algorithms, however, do not make full use of information provided by a population’s evolution, resulting in a low efficiency in generating test data. Objective: This paper emphasizes the problem of test data generation for parallel programs, and presents a feedback-directed genetic algorithm for generating test data of path coverage. Method: Information related to a schedule sequence is exploited to improve genetic operators. Specifically, a scheduling sequence is evaluated according to how well an individual covers the target path. The probability of the crossover and mutation points being located in the region is determined based on the evaluation result, which prevents a good schedule sequence from being destroyed. If crossover and mutation are performed in the scheduling sequence, the location of crossover and mutation points is further determined according to the relationship between nodes to be covered and the scheduling sequence. In this way, the population can be evolved in a narrowed search space. Results: The proposed algorithm is applied to test 11 parallel programs. The experimental results show that, compared with the genetic algorithm without utilizing information during the population evolution, the proposed algorithm significantly reduces the number of generations and the time consumption. Conclusion: The proposed algorithm can greatly improve the efficiency in evolutionary test data generation. Information utilization Genetic algorithm Parallel program Path coverage Test data Pan, Feng verfasserin aut Tian, Tian verfasserin aut Yang, Su verfasserin aut Meng, Fanlin verfasserin (orcid)0000-0002-4866-0011 aut Enthalten in Information & software technology Amsterdam [u.a.] : Elsevier Science, 1987 124 Online-Ressource (DE-627)320419185 (DE-600)2002332-7 (DE-576)259271160 nnns volume:124 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 85.00 Betriebswirtschaft: Allgemeines 54.50 Programmierung: Allgemeines AR 124
language	English
source	Enthalten in Information & software technology 124 volume:124
sourceStr	Enthalten in Information & software technology 124 volume:124
format_phy_str_mv	Article
bklname	Betriebswirtschaft: Allgemeines Programmierung: Allgemeines
institution	findex.gbv.de
topic_facet	Information utilization Genetic algorithm Parallel program Path coverage Test data
dewey-raw	330
isfreeaccess_bool	false
container_title	Information & software technology
authorswithroles_txt_mv	Gong, Dunwei @@aut@@ Pan, Feng @@aut@@ Tian, Tian @@aut@@ Yang, Su @@aut@@ Meng, Fanlin @@aut@@
publishDateDaySort_date	2020-01-01T00:00:00Z
hierarchy_top_id	320419185
dewey-sort	3330
id	ELV004105389
language_de	englisch
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author	Gong, Dunwei
spellingShingle	Gong, Dunwei ddc 330 bkl 85.00 bkl 54.50 misc Information utilization misc Genetic algorithm misc Parallel program misc Path coverage misc Test data A feedback-directed method of evolutionary test data generation for parallel programs
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topic_title	330 004 DE-600 85.00 bkl 54.50 bkl A feedback-directed method of evolutionary test data generation for parallel programs Information utilization Genetic algorithm Parallel program Path coverage Test data
topic	ddc 330 bkl 85.00 bkl 54.50 misc Information utilization misc Genetic algorithm misc Parallel program misc Path coverage misc Test data
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title	A feedback-directed method of evolutionary test data generation for parallel programs
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title_full	A feedback-directed method of evolutionary test data generation for parallel programs
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title_sort	a feedback-directed method of evolutionary test data generation for parallel programs
title_auth	A feedback-directed method of evolutionary test data generation for parallel programs
abstract	Context: Genetic algorithms can be utilized for automatic test data generation. Test data are encoded as individuals which are evolved for a number of generations using genetic operators. Test data of a parallel program include not only the program input, but also the communication information between each pair of processes. Traditional genetic algorithms, however, do not make full use of information provided by a population’s evolution, resulting in a low efficiency in generating test data. Objective: This paper emphasizes the problem of test data generation for parallel programs, and presents a feedback-directed genetic algorithm for generating test data of path coverage. Method: Information related to a schedule sequence is exploited to improve genetic operators. Specifically, a scheduling sequence is evaluated according to how well an individual covers the target path. The probability of the crossover and mutation points being located in the region is determined based on the evaluation result, which prevents a good schedule sequence from being destroyed. If crossover and mutation are performed in the scheduling sequence, the location of crossover and mutation points is further determined according to the relationship between nodes to be covered and the scheduling sequence. In this way, the population can be evolved in a narrowed search space. Results: The proposed algorithm is applied to test 11 parallel programs. The experimental results show that, compared with the genetic algorithm without utilizing information during the population evolution, the proposed algorithm significantly reduces the number of generations and the time consumption. Conclusion: The proposed algorithm can greatly improve the efficiency in evolutionary test data generation.
abstractGer	Context: Genetic algorithms can be utilized for automatic test data generation. Test data are encoded as individuals which are evolved for a number of generations using genetic operators. Test data of a parallel program include not only the program input, but also the communication information between each pair of processes. Traditional genetic algorithms, however, do not make full use of information provided by a population’s evolution, resulting in a low efficiency in generating test data. Objective: This paper emphasizes the problem of test data generation for parallel programs, and presents a feedback-directed genetic algorithm for generating test data of path coverage. Method: Information related to a schedule sequence is exploited to improve genetic operators. Specifically, a scheduling sequence is evaluated according to how well an individual covers the target path. The probability of the crossover and mutation points being located in the region is determined based on the evaluation result, which prevents a good schedule sequence from being destroyed. If crossover and mutation are performed in the scheduling sequence, the location of crossover and mutation points is further determined according to the relationship between nodes to be covered and the scheduling sequence. In this way, the population can be evolved in a narrowed search space. Results: The proposed algorithm is applied to test 11 parallel programs. The experimental results show that, compared with the genetic algorithm without utilizing information during the population evolution, the proposed algorithm significantly reduces the number of generations and the time consumption. Conclusion: The proposed algorithm can greatly improve the efficiency in evolutionary test data generation.
abstract_unstemmed	Context: Genetic algorithms can be utilized for automatic test data generation. Test data are encoded as individuals which are evolved for a number of generations using genetic operators. Test data of a parallel program include not only the program input, but also the communication information between each pair of processes. Traditional genetic algorithms, however, do not make full use of information provided by a population’s evolution, resulting in a low efficiency in generating test data. Objective: This paper emphasizes the problem of test data generation for parallel programs, and presents a feedback-directed genetic algorithm for generating test data of path coverage. Method: Information related to a schedule sequence is exploited to improve genetic operators. Specifically, a scheduling sequence is evaluated according to how well an individual covers the target path. The probability of the crossover and mutation points being located in the region is determined based on the evaluation result, which prevents a good schedule sequence from being destroyed. If crossover and mutation are performed in the scheduling sequence, the location of crossover and mutation points is further determined according to the relationship between nodes to be covered and the scheduling sequence. In this way, the population can be evolved in a narrowed search space. Results: The proposed algorithm is applied to test 11 parallel programs. The experimental results show that, compared with the genetic algorithm without utilizing information during the population evolution, the proposed algorithm significantly reduces the number of generations and the time consumption. Conclusion: The proposed algorithm can greatly improve the efficiency in evolutionary test data generation.
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title_short	A feedback-directed method of evolutionary test data generation for parallel programs
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author2	Pan, Feng Tian, Tian Yang, Su Meng, Fanlin
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doi_str	10.1016/j.infsof.2020.106318
up_date	2024-07-06T21:52:04.099Z
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A feedback-directed method of evolutionary test data generation for parallel programs

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