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
Autor*in: |
Gong, Dunwei [verfasserIn] Tian, Tian [verfasserIn] Wang, Jinxin [verfasserIn] Du, Ying [verfasserIn] Li, Zheng [verfasserIn] |
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Format: |
E-Artikel |
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Sprache: |
Englisch |
Erschienen: |
2020 |
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Schlagwörter: |
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Übergeordnetes Werk: |
Enthalten in: Parallel computing - Amsterdam [u.a.] : North-Holland, Elsevier Science, 1984, 97 |
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Übergeordnetes Werk: |
volume:97 |
DOI / URN: |
10.1016/j.parco.2020.102665 |
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Katalog-ID: |
ELV004618297 |
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245 | 1 | 0 | |a A novel method of grouping target paths for parallel programs |
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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 | |
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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 |
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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 |
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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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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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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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A novel method of grouping target paths for parallel programs |
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A novel method of grouping target paths for parallel programs |
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Gong, Dunwei |
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Parallel computing |
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Gong, Dunwei Tian, Tian Wang, Jinxin Du, Ying Li, Zheng |
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Gong, Dunwei |
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10.1016/j.parco.2020.102665 |
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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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