Using memetic algorithm for robustness testing of contract-based software models

Abstract Graph Transformation System (GTS) can formally specify the behavioral aspects of complex systems through graph-based contracts. Test suite generation under normal conditions from GTS specifications is a task well-suited to evolutionary algorithms such as Genetic and Particle Swarm Optimizat...
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Gespeichert in:

Autor*in:	Bahrampour, Anvar [verfasserIn] Rafe, Vahid [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2020

Schlagwörter:	Robustness testing Model testing Graph transformation specification Specification testing Coverage criteria

Übergeordnetes Werk:	Enthalten in: Artificial intelligence review - Dordrecht [u.a.] : Springer Science + Business Media B.V, 1986, 54(2020), 2 vom: 06. Aug., Seite 877-915
Übergeordnetes Werk:	volume:54 ; year:2020 ; number:2 ; day:06 ; month:08 ; pages:877-915

Links:	Volltext

DOI / URN:	10.1007/s10462-020-09881-y

Katalog-ID:	SPR043198953

Internformat


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520			\|a Abstract Graph Transformation System (GTS) can formally specify the behavioral aspects of complex systems through graph-based contracts. Test suite generation under normal conditions from GTS specifications is a task well-suited to evolutionary algorithms such as Genetic and Particle Swarm Optimization (PSO) metaheuristics. However, testing the vulnerabilities of a system under unexpected events such as invalid inputs is essential. Furthermore, the mentioned global search algorithms tend to make big jumps in the system’s state-space that are not concentrated on particular test goals. In this paper, we extend the HGAPSO approach into a cost-aware Memetic Algorithm (MA) by making small local changes through a proposed local search operator to optimize coverage score and testing costs. Moreover, we test GTS specifications not only under normal events but also under unexpected situations. So, three coverage-based testing strategies are investigated, including normal testing, robustness testing, and a hybrid strategy. The effectiveness of the proposed test generation algorithm and the testing strategies are evaluated through a type of mutation analysis at the model-level. Our experimental results show that (1) the hybrid testing strategy outperforms normal and robustness testing strategies in terms of fault-detection capability, (2) the robustness testing is the most cost-efficient strategy, and (3) the proposed MA with the hybrid testing strategy outperforms the state-of-the-art global search algorithms.
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700	1		\|a Rafe, Vahid \|e verfasserin \|4 aut
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matchkey_str	article:15737462:2020----::snmmtcloihfroutesetnocnrc
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bklnumber	54.72 77.31
publishDate	2020
allfields	10.1007/s10462-020-09881-y doi (DE-627)SPR043198953 (DE-599)SPRs10462-020-09881-y-e (SPR)s10462-020-09881-y-e DE-627 ger DE-627 rakwb eng 004 ASE 54.72 bkl 77.31 bkl Bahrampour, Anvar verfasserin aut Using memetic algorithm for robustness testing of contract-based software models 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Graph Transformation System (GTS) can formally specify the behavioral aspects of complex systems through graph-based contracts. Test suite generation under normal conditions from GTS specifications is a task well-suited to evolutionary algorithms such as Genetic and Particle Swarm Optimization (PSO) metaheuristics. However, testing the vulnerabilities of a system under unexpected events such as invalid inputs is essential. Furthermore, the mentioned global search algorithms tend to make big jumps in the system’s state-space that are not concentrated on particular test goals. In this paper, we extend the HGAPSO approach into a cost-aware Memetic Algorithm (MA) by making small local changes through a proposed local search operator to optimize coverage score and testing costs. Moreover, we test GTS specifications not only under normal events but also under unexpected situations. So, three coverage-based testing strategies are investigated, including normal testing, robustness testing, and a hybrid strategy. The effectiveness of the proposed test generation algorithm and the testing strategies are evaluated through a type of mutation analysis at the model-level. Our experimental results show that (1) the hybrid testing strategy outperforms normal and robustness testing strategies in terms of fault-detection capability, (2) the robustness testing is the most cost-efficient strategy, and (3) the proposed MA with the hybrid testing strategy outperforms the state-of-the-art global search algorithms. Robustness testing (dpeaa)DE-He213 Model testing (dpeaa)DE-He213 Graph transformation specification (dpeaa)DE-He213 Specification testing (dpeaa)DE-He213 Coverage criteria (dpeaa)DE-He213 Rafe, Vahid verfasserin aut Enthalten in Artificial intelligence review Dordrecht [u.a.] : Springer Science + Business Media B.V, 1986 54(2020), 2 vom: 06. Aug., Seite 877-915 (DE-627)27134945X (DE-600)1479828-1 1573-7462 nnns volume:54 year:2020 number:2 day:06 month:08 pages:877-915 https://dx.doi.org/10.1007/s10462-020-09881-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 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_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_165 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_1200 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 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_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 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_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.72 ASE 77.31 ASE AR 54 2020 2 06 08 877-915
spelling	10.1007/s10462-020-09881-y doi (DE-627)SPR043198953 (DE-599)SPRs10462-020-09881-y-e (SPR)s10462-020-09881-y-e DE-627 ger DE-627 rakwb eng 004 ASE 54.72 bkl 77.31 bkl Bahrampour, Anvar verfasserin aut Using memetic algorithm for robustness testing of contract-based software models 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Graph Transformation System (GTS) can formally specify the behavioral aspects of complex systems through graph-based contracts. Test suite generation under normal conditions from GTS specifications is a task well-suited to evolutionary algorithms such as Genetic and Particle Swarm Optimization (PSO) metaheuristics. However, testing the vulnerabilities of a system under unexpected events such as invalid inputs is essential. Furthermore, the mentioned global search algorithms tend to make big jumps in the system’s state-space that are not concentrated on particular test goals. In this paper, we extend the HGAPSO approach into a cost-aware Memetic Algorithm (MA) by making small local changes through a proposed local search operator to optimize coverage score and testing costs. Moreover, we test GTS specifications not only under normal events but also under unexpected situations. So, three coverage-based testing strategies are investigated, including normal testing, robustness testing, and a hybrid strategy. The effectiveness of the proposed test generation algorithm and the testing strategies are evaluated through a type of mutation analysis at the model-level. Our experimental results show that (1) the hybrid testing strategy outperforms normal and robustness testing strategies in terms of fault-detection capability, (2) the robustness testing is the most cost-efficient strategy, and (3) the proposed MA with the hybrid testing strategy outperforms the state-of-the-art global search algorithms. Robustness testing (dpeaa)DE-He213 Model testing (dpeaa)DE-He213 Graph transformation specification (dpeaa)DE-He213 Specification testing (dpeaa)DE-He213 Coverage criteria (dpeaa)DE-He213 Rafe, Vahid verfasserin aut Enthalten in Artificial intelligence review Dordrecht [u.a.] : Springer Science + Business Media B.V, 1986 54(2020), 2 vom: 06. Aug., Seite 877-915 (DE-627)27134945X (DE-600)1479828-1 1573-7462 nnns volume:54 year:2020 number:2 day:06 month:08 pages:877-915 https://dx.doi.org/10.1007/s10462-020-09881-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 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_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_165 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_1200 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 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_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 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_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.72 ASE 77.31 ASE AR 54 2020 2 06 08 877-915
allfields_unstemmed	10.1007/s10462-020-09881-y doi (DE-627)SPR043198953 (DE-599)SPRs10462-020-09881-y-e (SPR)s10462-020-09881-y-e DE-627 ger DE-627 rakwb eng 004 ASE 54.72 bkl 77.31 bkl Bahrampour, Anvar verfasserin aut Using memetic algorithm for robustness testing of contract-based software models 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Graph Transformation System (GTS) can formally specify the behavioral aspects of complex systems through graph-based contracts. Test suite generation under normal conditions from GTS specifications is a task well-suited to evolutionary algorithms such as Genetic and Particle Swarm Optimization (PSO) metaheuristics. However, testing the vulnerabilities of a system under unexpected events such as invalid inputs is essential. Furthermore, the mentioned global search algorithms tend to make big jumps in the system’s state-space that are not concentrated on particular test goals. In this paper, we extend the HGAPSO approach into a cost-aware Memetic Algorithm (MA) by making small local changes through a proposed local search operator to optimize coverage score and testing costs. Moreover, we test GTS specifications not only under normal events but also under unexpected situations. So, three coverage-based testing strategies are investigated, including normal testing, robustness testing, and a hybrid strategy. The effectiveness of the proposed test generation algorithm and the testing strategies are evaluated through a type of mutation analysis at the model-level. Our experimental results show that (1) the hybrid testing strategy outperforms normal and robustness testing strategies in terms of fault-detection capability, (2) the robustness testing is the most cost-efficient strategy, and (3) the proposed MA with the hybrid testing strategy outperforms the state-of-the-art global search algorithms. Robustness testing (dpeaa)DE-He213 Model testing (dpeaa)DE-He213 Graph transformation specification (dpeaa)DE-He213 Specification testing (dpeaa)DE-He213 Coverage criteria (dpeaa)DE-He213 Rafe, Vahid verfasserin aut Enthalten in Artificial intelligence review Dordrecht [u.a.] : Springer Science + Business Media B.V, 1986 54(2020), 2 vom: 06. Aug., Seite 877-915 (DE-627)27134945X (DE-600)1479828-1 1573-7462 nnns volume:54 year:2020 number:2 day:06 month:08 pages:877-915 https://dx.doi.org/10.1007/s10462-020-09881-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 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_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_165 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_1200 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 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_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 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_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.72 ASE 77.31 ASE AR 54 2020 2 06 08 877-915
allfieldsGer	10.1007/s10462-020-09881-y doi (DE-627)SPR043198953 (DE-599)SPRs10462-020-09881-y-e (SPR)s10462-020-09881-y-e DE-627 ger DE-627 rakwb eng 004 ASE 54.72 bkl 77.31 bkl Bahrampour, Anvar verfasserin aut Using memetic algorithm for robustness testing of contract-based software models 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Graph Transformation System (GTS) can formally specify the behavioral aspects of complex systems through graph-based contracts. Test suite generation under normal conditions from GTS specifications is a task well-suited to evolutionary algorithms such as Genetic and Particle Swarm Optimization (PSO) metaheuristics. However, testing the vulnerabilities of a system under unexpected events such as invalid inputs is essential. Furthermore, the mentioned global search algorithms tend to make big jumps in the system’s state-space that are not concentrated on particular test goals. In this paper, we extend the HGAPSO approach into a cost-aware Memetic Algorithm (MA) by making small local changes through a proposed local search operator to optimize coverage score and testing costs. Moreover, we test GTS specifications not only under normal events but also under unexpected situations. So, three coverage-based testing strategies are investigated, including normal testing, robustness testing, and a hybrid strategy. The effectiveness of the proposed test generation algorithm and the testing strategies are evaluated through a type of mutation analysis at the model-level. Our experimental results show that (1) the hybrid testing strategy outperforms normal and robustness testing strategies in terms of fault-detection capability, (2) the robustness testing is the most cost-efficient strategy, and (3) the proposed MA with the hybrid testing strategy outperforms the state-of-the-art global search algorithms. Robustness testing (dpeaa)DE-He213 Model testing (dpeaa)DE-He213 Graph transformation specification (dpeaa)DE-He213 Specification testing (dpeaa)DE-He213 Coverage criteria (dpeaa)DE-He213 Rafe, Vahid verfasserin aut Enthalten in Artificial intelligence review Dordrecht [u.a.] : Springer Science + Business Media B.V, 1986 54(2020), 2 vom: 06. Aug., Seite 877-915 (DE-627)27134945X (DE-600)1479828-1 1573-7462 nnns volume:54 year:2020 number:2 day:06 month:08 pages:877-915 https://dx.doi.org/10.1007/s10462-020-09881-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 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_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_165 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_1200 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 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_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 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_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.72 ASE 77.31 ASE AR 54 2020 2 06 08 877-915
allfieldsSound	10.1007/s10462-020-09881-y doi (DE-627)SPR043198953 (DE-599)SPRs10462-020-09881-y-e (SPR)s10462-020-09881-y-e DE-627 ger DE-627 rakwb eng 004 ASE 54.72 bkl 77.31 bkl Bahrampour, Anvar verfasserin aut Using memetic algorithm for robustness testing of contract-based software models 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Graph Transformation System (GTS) can formally specify the behavioral aspects of complex systems through graph-based contracts. Test suite generation under normal conditions from GTS specifications is a task well-suited to evolutionary algorithms such as Genetic and Particle Swarm Optimization (PSO) metaheuristics. However, testing the vulnerabilities of a system under unexpected events such as invalid inputs is essential. Furthermore, the mentioned global search algorithms tend to make big jumps in the system’s state-space that are not concentrated on particular test goals. In this paper, we extend the HGAPSO approach into a cost-aware Memetic Algorithm (MA) by making small local changes through a proposed local search operator to optimize coverage score and testing costs. Moreover, we test GTS specifications not only under normal events but also under unexpected situations. So, three coverage-based testing strategies are investigated, including normal testing, robustness testing, and a hybrid strategy. The effectiveness of the proposed test generation algorithm and the testing strategies are evaluated through a type of mutation analysis at the model-level. Our experimental results show that (1) the hybrid testing strategy outperforms normal and robustness testing strategies in terms of fault-detection capability, (2) the robustness testing is the most cost-efficient strategy, and (3) the proposed MA with the hybrid testing strategy outperforms the state-of-the-art global search algorithms. Robustness testing (dpeaa)DE-He213 Model testing (dpeaa)DE-He213 Graph transformation specification (dpeaa)DE-He213 Specification testing (dpeaa)DE-He213 Coverage criteria (dpeaa)DE-He213 Rafe, Vahid verfasserin aut Enthalten in Artificial intelligence review Dordrecht [u.a.] : Springer Science + Business Media B.V, 1986 54(2020), 2 vom: 06. Aug., Seite 877-915 (DE-627)27134945X (DE-600)1479828-1 1573-7462 nnns volume:54 year:2020 number:2 day:06 month:08 pages:877-915 https://dx.doi.org/10.1007/s10462-020-09881-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 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_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_165 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_1200 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 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_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 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_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.72 ASE 77.31 ASE AR 54 2020 2 06 08 877-915
language	English
source	Enthalten in Artificial intelligence review 54(2020), 2 vom: 06. Aug., Seite 877-915 volume:54 year:2020 number:2 day:06 month:08 pages:877-915
sourceStr	Enthalten in Artificial intelligence review 54(2020), 2 vom: 06. Aug., Seite 877-915 volume:54 year:2020 number:2 day:06 month:08 pages:877-915
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Robustness testing Model testing Graph transformation specification Specification testing Coverage criteria
dewey-raw	004
isfreeaccess_bool	false
container_title	Artificial intelligence review
authorswithroles_txt_mv	Bahrampour, Anvar @@aut@@ Rafe, Vahid @@aut@@
publishDateDaySort_date	2020-08-06T00:00:00Z
hierarchy_top_id	27134945X
dewey-sort	14
id	SPR043198953
language_de	englisch
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author	Bahrampour, Anvar
spellingShingle	Bahrampour, Anvar ddc 004 bkl 54.72 bkl 77.31 misc Robustness testing misc Model testing misc Graph transformation specification misc Specification testing misc Coverage criteria Using memetic algorithm for robustness testing of contract-based software models
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topic_title	004 ASE 54.72 bkl 77.31 bkl Using memetic algorithm for robustness testing of contract-based software models Robustness testing (dpeaa)DE-He213 Model testing (dpeaa)DE-He213 Graph transformation specification (dpeaa)DE-He213 Specification testing (dpeaa)DE-He213 Coverage criteria (dpeaa)DE-He213
topic	ddc 004 bkl 54.72 bkl 77.31 misc Robustness testing misc Model testing misc Graph transformation specification misc Specification testing misc Coverage criteria
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title	Using memetic algorithm for robustness testing of contract-based software models
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title_full	Using memetic algorithm for robustness testing of contract-based software models
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title_sort	using memetic algorithm for robustness testing of contract-based software models
title_auth	Using memetic algorithm for robustness testing of contract-based software models
abstract	Abstract Graph Transformation System (GTS) can formally specify the behavioral aspects of complex systems through graph-based contracts. Test suite generation under normal conditions from GTS specifications is a task well-suited to evolutionary algorithms such as Genetic and Particle Swarm Optimization (PSO) metaheuristics. However, testing the vulnerabilities of a system under unexpected events such as invalid inputs is essential. Furthermore, the mentioned global search algorithms tend to make big jumps in the system’s state-space that are not concentrated on particular test goals. In this paper, we extend the HGAPSO approach into a cost-aware Memetic Algorithm (MA) by making small local changes through a proposed local search operator to optimize coverage score and testing costs. Moreover, we test GTS specifications not only under normal events but also under unexpected situations. So, three coverage-based testing strategies are investigated, including normal testing, robustness testing, and a hybrid strategy. The effectiveness of the proposed test generation algorithm and the testing strategies are evaluated through a type of mutation analysis at the model-level. Our experimental results show that (1) the hybrid testing strategy outperforms normal and robustness testing strategies in terms of fault-detection capability, (2) the robustness testing is the most cost-efficient strategy, and (3) the proposed MA with the hybrid testing strategy outperforms the state-of-the-art global search algorithms.
abstractGer	Abstract Graph Transformation System (GTS) can formally specify the behavioral aspects of complex systems through graph-based contracts. Test suite generation under normal conditions from GTS specifications is a task well-suited to evolutionary algorithms such as Genetic and Particle Swarm Optimization (PSO) metaheuristics. However, testing the vulnerabilities of a system under unexpected events such as invalid inputs is essential. Furthermore, the mentioned global search algorithms tend to make big jumps in the system’s state-space that are not concentrated on particular test goals. In this paper, we extend the HGAPSO approach into a cost-aware Memetic Algorithm (MA) by making small local changes through a proposed local search operator to optimize coverage score and testing costs. Moreover, we test GTS specifications not only under normal events but also under unexpected situations. So, three coverage-based testing strategies are investigated, including normal testing, robustness testing, and a hybrid strategy. The effectiveness of the proposed test generation algorithm and the testing strategies are evaluated through a type of mutation analysis at the model-level. Our experimental results show that (1) the hybrid testing strategy outperforms normal and robustness testing strategies in terms of fault-detection capability, (2) the robustness testing is the most cost-efficient strategy, and (3) the proposed MA with the hybrid testing strategy outperforms the state-of-the-art global search algorithms.
abstract_unstemmed	Abstract Graph Transformation System (GTS) can formally specify the behavioral aspects of complex systems through graph-based contracts. Test suite generation under normal conditions from GTS specifications is a task well-suited to evolutionary algorithms such as Genetic and Particle Swarm Optimization (PSO) metaheuristics. However, testing the vulnerabilities of a system under unexpected events such as invalid inputs is essential. Furthermore, the mentioned global search algorithms tend to make big jumps in the system’s state-space that are not concentrated on particular test goals. In this paper, we extend the HGAPSO approach into a cost-aware Memetic Algorithm (MA) by making small local changes through a proposed local search operator to optimize coverage score and testing costs. Moreover, we test GTS specifications not only under normal events but also under unexpected situations. So, three coverage-based testing strategies are investigated, including normal testing, robustness testing, and a hybrid strategy. The effectiveness of the proposed test generation algorithm and the testing strategies are evaluated through a type of mutation analysis at the model-level. Our experimental results show that (1) the hybrid testing strategy outperforms normal and robustness testing strategies in terms of fault-detection capability, (2) the robustness testing is the most cost-efficient strategy, and (3) the proposed MA with the hybrid testing strategy outperforms the state-of-the-art global search algorithms.
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title_short	Using memetic algorithm for robustness testing of contract-based software models
url	https://dx.doi.org/10.1007/s10462-020-09881-y
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doi_str	10.1007/s10462-020-09881-y
up_date	2024-07-03T17:10:45.194Z
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Test suite generation under normal conditions from GTS specifications is a task well-suited to evolutionary algorithms such as Genetic and Particle Swarm Optimization (PSO) metaheuristics. However, testing the vulnerabilities of a system under unexpected events such as invalid inputs is essential. Furthermore, the mentioned global search algorithms tend to make big jumps in the system’s state-space that are not concentrated on particular test goals. In this paper, we extend the HGAPSO approach into a cost-aware Memetic Algorithm (MA) by making small local changes through a proposed local search operator to optimize coverage score and testing costs. Moreover, we test GTS specifications not only under normal events but also under unexpected situations. So, three coverage-based testing strategies are investigated, including normal testing, robustness testing, and a hybrid strategy. The effectiveness of the proposed test generation algorithm and the testing strategies are evaluated through a type of mutation analysis at the model-level. Our experimental results show that (1) the hybrid testing strategy outperforms normal and robustness testing strategies in terms of fault-detection capability, (2) the robustness testing is the most cost-efficient strategy, and (3) the proposed MA with the hybrid testing strategy outperforms the state-of-the-art global search algorithms.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Robustness testing</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Model testing</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Graph transformation specification</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Specification testing</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Coverage criteria</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Rafe, Vahid</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Artificial intelligence review</subfield><subfield code="d">Dordrecht [u.a.] : Springer Science + Business Media B.V, 1986</subfield><subfield code="g">54(2020), 2 vom: 06. 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Using memetic algorithm for robustness testing of contract-based software models

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