A novel method of grouping target paths for parallel programs

Genetic algorithms can be employed to automatically generate desired test data, with the advantage of freeing up manpower. For the path coverage criterion, the problem of test data generation needs to be transformed into an optimization problem before applying genetic algorithms. However, when the n...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Gong, Dunwei [verfasserIn] Tian, Tian [verfasserIn] Wang, Jinxin [verfasserIn] Du, Ying [verfasserIn] Li, Zheng [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2020

Schlagwörter:	MPI Parallel program Genetic algorithms Similarity of paths Grouping paths Benchmark path Test data generation

Übergeordnetes Werk:	Enthalten in: Parallel computing - Amsterdam [u.a.] : North-Holland, Elsevier Science, 1984, 97
Übergeordnetes Werk:	volume:97

DOI / URN:	10.1016/j.parco.2020.102665

Katalog-ID:	ELV004618297

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100	1		\|a Gong, Dunwei \|e verfasserin \|4 aut
245	1	0	\|a A novel method of grouping target paths for parallel programs
264		1	\|c 2020
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337			\|a Computermedien \|b c \|2 rdamedia
338			\|a Online-Ressource \|b cr \|2 rdacarrier
520			\|a Genetic algorithms can be employed to automatically generate desired test data, with the advantage of freeing up manpower. For the path coverage criterion, the problem of test data generation needs to be transformed into an optimization problem before applying genetic algorithms. However, when the number of paths to be covered is large, the transformed optimization problem will be very complicated. Correspondingly, the difficulty of problem solving will be greatly increased. In view of this, the complex optimization problem is divided into a number of sub-optimization problems by grouping paths. However, the existing method of grouping paths has not fully taken the characteristic of multiple processes existing in a parallel program into consideration. As a result, inappropriate paths will be put into the same group, which heavily restricts the efficiency of test data generation. To overcome the above drawback, this study proposes a novel method of grouping paths. This method refines the measurement of the path similarity when grouping target paths, dynamically increases the number of benchmark paths in a group, and groups the remaining ones based on the similarity between a path and each of these benchmark paths, with the purpose of a large similarity between each pair of paths in the same group. The proposed method is applied to test nine typical programs, and compared with the method of randomly grouping paths and the existing method of grouping paths. The experimental results show that paths in the same group obtained by the proposed method have a larger similarity, which is beneficial to efficiently generating test data that satisfy the path coverage criterion.
650		4	\|a MPI Parallel program
650		4	\|a Genetic algorithms
650		4	\|a Similarity of paths
650		4	\|a Grouping paths
650		4	\|a Benchmark path
650		4	\|a Test data generation
700	1		\|a Tian, Tian \|e verfasserin \|4 aut
700	1		\|a Wang, Jinxin \|e verfasserin \|4 aut
700	1		\|a Du, Ying \|e verfasserin \|4 aut
700	1		\|a Li, Zheng \|e verfasserin \|4 aut
773	0	8	\|i Enthalten in \|t Parallel computing \|d Amsterdam [u.a.] : North-Holland, Elsevier Science, 1984 \|g 97 \|h Online-Ressource \|w (DE-627)265784115 \|w (DE-600)1466340-5 \|w (DE-576)074890999 \|x 1872-7336 \|7 nnns
773	1	8	\|g volume:97
912			\|a GBV_USEFLAG_U
912			\|a SYSFLAG_U
912			\|a GBV_ELV
912			\|a GBV_ILN_20
912			\|a GBV_ILN_22
912			\|a GBV_ILN_23
912			\|a GBV_ILN_24
912			\|a GBV_ILN_31
912			\|a GBV_ILN_32
912			\|a GBV_ILN_40
912			\|a GBV_ILN_60
912			\|a GBV_ILN_62
912			\|a GBV_ILN_63
912			\|a GBV_ILN_65
912			\|a GBV_ILN_69
912			\|a GBV_ILN_70
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
912			\|a GBV_ILN_2021
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
912			\|a GBV_ILN_2050
912			\|a GBV_ILN_2056
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
912			\|a GBV_ILN_2111
912			\|a GBV_ILN_2112
912			\|a GBV_ILN_2113
912			\|a GBV_ILN_2118
912			\|a GBV_ILN_2122
912			\|a GBV_ILN_2129
912			\|a GBV_ILN_2143
912			\|a GBV_ILN_2147
912			\|a GBV_ILN_2148
912			\|a GBV_ILN_2152
912			\|a GBV_ILN_2153
912			\|a GBV_ILN_2190
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
912			\|a GBV_ILN_4242
912			\|a GBV_ILN_4251
912			\|a GBV_ILN_4305
912			\|a GBV_ILN_4313
912			\|a GBV_ILN_4322
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 54.25 \|j Parallele Datenverarbeitung
951			\|a AR
952			\|d 97

Indexfelder

author_variant	d g dg t t tt j w jw y d yd z l zl
matchkey_str	article:18727336:2020----::nvlehdfruigagtahfra
hierarchy_sort_str	2020
bklnumber	54.25
publishDate	2020
allfields	10.1016/j.parco.2020.102665 doi (DE-627)ELV004618297 (ELSEVIER)S0167-8191(20)30058-2 DE-627 ger DE-627 rda eng 004 620 DE-600 54.25 bkl Gong, Dunwei verfasserin aut A novel method of grouping target paths for parallel programs 2020 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Genetic algorithms can be employed to automatically generate desired test data, with the advantage of freeing up manpower. For the path coverage criterion, the problem of test data generation needs to be transformed into an optimization problem before applying genetic algorithms. However, when the number of paths to be covered is large, the transformed optimization problem will be very complicated. Correspondingly, the difficulty of problem solving will be greatly increased. In view of this, the complex optimization problem is divided into a number of sub-optimization problems by grouping paths. However, the existing method of grouping paths has not fully taken the characteristic of multiple processes existing in a parallel program into consideration. As a result, inappropriate paths will be put into the same group, which heavily restricts the efficiency of test data generation. To overcome the above drawback, this study proposes a novel method of grouping paths. This method refines the measurement of the path similarity when grouping target paths, dynamically increases the number of benchmark paths in a group, and groups the remaining ones based on the similarity between a path and each of these benchmark paths, with the purpose of a large similarity between each pair of paths in the same group. The proposed method is applied to test nine typical programs, and compared with the method of randomly grouping paths and the existing method of grouping paths. The experimental results show that paths in the same group obtained by the proposed method have a larger similarity, which is beneficial to efficiently generating test data that satisfy the path coverage criterion. MPI Parallel program Genetic algorithms Similarity of paths Grouping paths Benchmark path Test data generation Tian, Tian verfasserin aut Wang, Jinxin verfasserin aut Du, Ying verfasserin aut Li, Zheng verfasserin aut Enthalten in Parallel computing Amsterdam [u.a.] : North-Holland, Elsevier Science, 1984 97 Online-Ressource (DE-627)265784115 (DE-600)1466340-5 (DE-576)074890999 1872-7336 nnns volume:97 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_4322 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 54.25 Parallele Datenverarbeitung AR 97
spelling	10.1016/j.parco.2020.102665 doi (DE-627)ELV004618297 (ELSEVIER)S0167-8191(20)30058-2 DE-627 ger DE-627 rda eng 004 620 DE-600 54.25 bkl Gong, Dunwei verfasserin aut A novel method of grouping target paths for parallel programs 2020 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Genetic algorithms can be employed to automatically generate desired test data, with the advantage of freeing up manpower. For the path coverage criterion, the problem of test data generation needs to be transformed into an optimization problem before applying genetic algorithms. However, when the number of paths to be covered is large, the transformed optimization problem will be very complicated. Correspondingly, the difficulty of problem solving will be greatly increased. In view of this, the complex optimization problem is divided into a number of sub-optimization problems by grouping paths. However, the existing method of grouping paths has not fully taken the characteristic of multiple processes existing in a parallel program into consideration. As a result, inappropriate paths will be put into the same group, which heavily restricts the efficiency of test data generation. To overcome the above drawback, this study proposes a novel method of grouping paths. This method refines the measurement of the path similarity when grouping target paths, dynamically increases the number of benchmark paths in a group, and groups the remaining ones based on the similarity between a path and each of these benchmark paths, with the purpose of a large similarity between each pair of paths in the same group. The proposed method is applied to test nine typical programs, and compared with the method of randomly grouping paths and the existing method of grouping paths. The experimental results show that paths in the same group obtained by the proposed method have a larger similarity, which is beneficial to efficiently generating test data that satisfy the path coverage criterion. MPI Parallel program Genetic algorithms Similarity of paths Grouping paths Benchmark path Test data generation Tian, Tian verfasserin aut Wang, Jinxin verfasserin aut Du, Ying verfasserin aut Li, Zheng verfasserin aut Enthalten in Parallel computing Amsterdam [u.a.] : North-Holland, Elsevier Science, 1984 97 Online-Ressource (DE-627)265784115 (DE-600)1466340-5 (DE-576)074890999 1872-7336 nnns volume:97 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_4322 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 54.25 Parallele Datenverarbeitung AR 97
allfields_unstemmed	10.1016/j.parco.2020.102665 doi (DE-627)ELV004618297 (ELSEVIER)S0167-8191(20)30058-2 DE-627 ger DE-627 rda eng 004 620 DE-600 54.25 bkl Gong, Dunwei verfasserin aut A novel method of grouping target paths for parallel programs 2020 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Genetic algorithms can be employed to automatically generate desired test data, with the advantage of freeing up manpower. For the path coverage criterion, the problem of test data generation needs to be transformed into an optimization problem before applying genetic algorithms. However, when the number of paths to be covered is large, the transformed optimization problem will be very complicated. Correspondingly, the difficulty of problem solving will be greatly increased. In view of this, the complex optimization problem is divided into a number of sub-optimization problems by grouping paths. However, the existing method of grouping paths has not fully taken the characteristic of multiple processes existing in a parallel program into consideration. As a result, inappropriate paths will be put into the same group, which heavily restricts the efficiency of test data generation. To overcome the above drawback, this study proposes a novel method of grouping paths. This method refines the measurement of the path similarity when grouping target paths, dynamically increases the number of benchmark paths in a group, and groups the remaining ones based on the similarity between a path and each of these benchmark paths, with the purpose of a large similarity between each pair of paths in the same group. The proposed method is applied to test nine typical programs, and compared with the method of randomly grouping paths and the existing method of grouping paths. The experimental results show that paths in the same group obtained by the proposed method have a larger similarity, which is beneficial to efficiently generating test data that satisfy the path coverage criterion. MPI Parallel program Genetic algorithms Similarity of paths Grouping paths Benchmark path Test data generation Tian, Tian verfasserin aut Wang, Jinxin verfasserin aut Du, Ying verfasserin aut Li, Zheng verfasserin aut Enthalten in Parallel computing Amsterdam [u.a.] : North-Holland, Elsevier Science, 1984 97 Online-Ressource (DE-627)265784115 (DE-600)1466340-5 (DE-576)074890999 1872-7336 nnns volume:97 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_4322 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 54.25 Parallele Datenverarbeitung AR 97
allfieldsGer	10.1016/j.parco.2020.102665 doi (DE-627)ELV004618297 (ELSEVIER)S0167-8191(20)30058-2 DE-627 ger DE-627 rda eng 004 620 DE-600 54.25 bkl Gong, Dunwei verfasserin aut A novel method of grouping target paths for parallel programs 2020 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Genetic algorithms can be employed to automatically generate desired test data, with the advantage of freeing up manpower. For the path coverage criterion, the problem of test data generation needs to be transformed into an optimization problem before applying genetic algorithms. However, when the number of paths to be covered is large, the transformed optimization problem will be very complicated. Correspondingly, the difficulty of problem solving will be greatly increased. In view of this, the complex optimization problem is divided into a number of sub-optimization problems by grouping paths. However, the existing method of grouping paths has not fully taken the characteristic of multiple processes existing in a parallel program into consideration. As a result, inappropriate paths will be put into the same group, which heavily restricts the efficiency of test data generation. To overcome the above drawback, this study proposes a novel method of grouping paths. This method refines the measurement of the path similarity when grouping target paths, dynamically increases the number of benchmark paths in a group, and groups the remaining ones based on the similarity between a path and each of these benchmark paths, with the purpose of a large similarity between each pair of paths in the same group. The proposed method is applied to test nine typical programs, and compared with the method of randomly grouping paths and the existing method of grouping paths. The experimental results show that paths in the same group obtained by the proposed method have a larger similarity, which is beneficial to efficiently generating test data that satisfy the path coverage criterion. MPI Parallel program Genetic algorithms Similarity of paths Grouping paths Benchmark path Test data generation Tian, Tian verfasserin aut Wang, Jinxin verfasserin aut Du, Ying verfasserin aut Li, Zheng verfasserin aut Enthalten in Parallel computing Amsterdam [u.a.] : North-Holland, Elsevier Science, 1984 97 Online-Ressource (DE-627)265784115 (DE-600)1466340-5 (DE-576)074890999 1872-7336 nnns volume:97 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_4322 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 54.25 Parallele Datenverarbeitung AR 97
allfieldsSound	10.1016/j.parco.2020.102665 doi (DE-627)ELV004618297 (ELSEVIER)S0167-8191(20)30058-2 DE-627 ger DE-627 rda eng 004 620 DE-600 54.25 bkl Gong, Dunwei verfasserin aut A novel method of grouping target paths for parallel programs 2020 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Genetic algorithms can be employed to automatically generate desired test data, with the advantage of freeing up manpower. For the path coverage criterion, the problem of test data generation needs to be transformed into an optimization problem before applying genetic algorithms. However, when the number of paths to be covered is large, the transformed optimization problem will be very complicated. Correspondingly, the difficulty of problem solving will be greatly increased. In view of this, the complex optimization problem is divided into a number of sub-optimization problems by grouping paths. However, the existing method of grouping paths has not fully taken the characteristic of multiple processes existing in a parallel program into consideration. As a result, inappropriate paths will be put into the same group, which heavily restricts the efficiency of test data generation. To overcome the above drawback, this study proposes a novel method of grouping paths. This method refines the measurement of the path similarity when grouping target paths, dynamically increases the number of benchmark paths in a group, and groups the remaining ones based on the similarity between a path and each of these benchmark paths, with the purpose of a large similarity between each pair of paths in the same group. The proposed method is applied to test nine typical programs, and compared with the method of randomly grouping paths and the existing method of grouping paths. The experimental results show that paths in the same group obtained by the proposed method have a larger similarity, which is beneficial to efficiently generating test data that satisfy the path coverage criterion. MPI Parallel program Genetic algorithms Similarity of paths Grouping paths Benchmark path Test data generation Tian, Tian verfasserin aut Wang, Jinxin verfasserin aut Du, Ying verfasserin aut Li, Zheng verfasserin aut Enthalten in Parallel computing Amsterdam [u.a.] : North-Holland, Elsevier Science, 1984 97 Online-Ressource (DE-627)265784115 (DE-600)1466340-5 (DE-576)074890999 1872-7336 nnns volume:97 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_4322 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 54.25 Parallele Datenverarbeitung AR 97
language	English
source	Enthalten in Parallel computing 97 volume:97
sourceStr	Enthalten in Parallel computing 97 volume:97
format_phy_str_mv	Article
bklname	Parallele Datenverarbeitung
institution	findex.gbv.de
topic_facet	MPI Parallel program Genetic algorithms Similarity of paths Grouping paths Benchmark path Test data generation
dewey-raw	004
isfreeaccess_bool	false
container_title	Parallel computing
authorswithroles_txt_mv	Gong, Dunwei @@aut@@ Tian, Tian @@aut@@ Wang, Jinxin @@aut@@ Du, Ying @@aut@@ Li, Zheng @@aut@@
publishDateDaySort_date	2020-01-01T00:00:00Z
hierarchy_top_id	265784115
dewey-sort	14
id	ELV004618297
language_de	englisch
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author	Gong, Dunwei
spellingShingle	Gong, Dunwei ddc 004 bkl 54.25 misc MPI Parallel program misc Genetic algorithms misc Similarity of paths misc Grouping paths misc Benchmark path misc Test data generation A novel method of grouping target paths for parallel programs
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topic_title	004 620 DE-600 54.25 bkl A novel method of grouping target paths for parallel programs MPI Parallel program Genetic algorithms Similarity of paths Grouping paths Benchmark path Test data generation
topic	ddc 004 bkl 54.25 misc MPI Parallel program misc Genetic algorithms misc Similarity of paths misc Grouping paths misc Benchmark path misc Test data generation
topic_unstemmed	ddc 004 bkl 54.25 misc MPI Parallel program misc Genetic algorithms misc Similarity of paths misc Grouping paths misc Benchmark path misc Test data generation
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title	A novel method of grouping target paths for parallel programs
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title_full	A novel method of grouping target paths for parallel programs
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author_browse	Gong, Dunwei Tian, Tian Wang, Jinxin Du, Ying Li, Zheng
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title_sort	a novel method of grouping target paths for parallel programs
title_auth	A novel method of grouping target paths for parallel programs
abstract	Genetic algorithms can be employed to automatically generate desired test data, with the advantage of freeing up manpower. For the path coverage criterion, the problem of test data generation needs to be transformed into an optimization problem before applying genetic algorithms. However, when the number of paths to be covered is large, the transformed optimization problem will be very complicated. Correspondingly, the difficulty of problem solving will be greatly increased. In view of this, the complex optimization problem is divided into a number of sub-optimization problems by grouping paths. However, the existing method of grouping paths has not fully taken the characteristic of multiple processes existing in a parallel program into consideration. As a result, inappropriate paths will be put into the same group, which heavily restricts the efficiency of test data generation. To overcome the above drawback, this study proposes a novel method of grouping paths. This method refines the measurement of the path similarity when grouping target paths, dynamically increases the number of benchmark paths in a group, and groups the remaining ones based on the similarity between a path and each of these benchmark paths, with the purpose of a large similarity between each pair of paths in the same group. The proposed method is applied to test nine typical programs, and compared with the method of randomly grouping paths and the existing method of grouping paths. The experimental results show that paths in the same group obtained by the proposed method have a larger similarity, which is beneficial to efficiently generating test data that satisfy the path coverage criterion.
abstractGer	Genetic algorithms can be employed to automatically generate desired test data, with the advantage of freeing up manpower. For the path coverage criterion, the problem of test data generation needs to be transformed into an optimization problem before applying genetic algorithms. However, when the number of paths to be covered is large, the transformed optimization problem will be very complicated. Correspondingly, the difficulty of problem solving will be greatly increased. In view of this, the complex optimization problem is divided into a number of sub-optimization problems by grouping paths. However, the existing method of grouping paths has not fully taken the characteristic of multiple processes existing in a parallel program into consideration. As a result, inappropriate paths will be put into the same group, which heavily restricts the efficiency of test data generation. To overcome the above drawback, this study proposes a novel method of grouping paths. This method refines the measurement of the path similarity when grouping target paths, dynamically increases the number of benchmark paths in a group, and groups the remaining ones based on the similarity between a path and each of these benchmark paths, with the purpose of a large similarity between each pair of paths in the same group. The proposed method is applied to test nine typical programs, and compared with the method of randomly grouping paths and the existing method of grouping paths. The experimental results show that paths in the same group obtained by the proposed method have a larger similarity, which is beneficial to efficiently generating test data that satisfy the path coverage criterion.
abstract_unstemmed	Genetic algorithms can be employed to automatically generate desired test data, with the advantage of freeing up manpower. For the path coverage criterion, the problem of test data generation needs to be transformed into an optimization problem before applying genetic algorithms. However, when the number of paths to be covered is large, the transformed optimization problem will be very complicated. Correspondingly, the difficulty of problem solving will be greatly increased. In view of this, the complex optimization problem is divided into a number of sub-optimization problems by grouping paths. However, the existing method of grouping paths has not fully taken the characteristic of multiple processes existing in a parallel program into consideration. As a result, inappropriate paths will be put into the same group, which heavily restricts the efficiency of test data generation. To overcome the above drawback, this study proposes a novel method of grouping paths. This method refines the measurement of the path similarity when grouping target paths, dynamically increases the number of benchmark paths in a group, and groups the remaining ones based on the similarity between a path and each of these benchmark paths, with the purpose of a large similarity between each pair of paths in the same group. The proposed method is applied to test nine typical programs, and compared with the method of randomly grouping paths and the existing method of grouping paths. The experimental results show that paths in the same group obtained by the proposed method have a larger similarity, which is beneficial to efficiently generating test data that satisfy the path coverage criterion.
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title_short	A novel method of grouping target paths for parallel programs
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author2	Tian, Tian Wang, Jinxin Du, Ying Li, Zheng
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doi_str	10.1016/j.parco.2020.102665
up_date	2024-07-06T23:35:54.391Z
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A novel method of grouping target paths for parallel programs

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