MOPISDE: A collaborative multi-objective information-sharing DE algorithm for software clustering

The software module clustering problem (SMCP) aims to improve the internal quality of software while helping software engineers understand the system architecture and facilitating software system maintenance. However, most current methods ignore modular stability in software evolution and the topolo...
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Autor*in:	Kang, Yan [verfasserIn] Xie, Wentao [verfasserIn] Wang, Xiaopeng [verfasserIn] Wang, Haining [verfasserIn] Wang, Xinchao [verfasserIn] Li, Jinyuan [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	Software modularization Multi-objective optimization Differential evolution algorithm Software architecture Software understanding Software system maintenance

Übergeordnetes Werk:	Enthalten in: Expert systems with applications - Amsterdam [u.a.] : Elsevier Science, 1990, 226
Übergeordnetes Werk:	volume:226

DOI / URN:	10.1016/j.eswa.2023.120207

Katalog-ID:	ELV009789308

Internformat


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520			\|a The software module clustering problem (SMCP) aims to improve the internal quality of software while helping software engineers understand the system architecture and facilitating software system maintenance. However, most current methods ignore modular stability in software evolution and the topological properties of the software architecture and hence obtain decompositions much worse than the expert. Therefore, we propose a collaborative multi-objective information-sharing differential evolution (MOPISDE) algorithm for SMCP with global stability and path complexity as two new objective functions. Specifically, two new concepts are defined for SMCP as two objectives of populations and modular quality (MQ) as an objective of the third population. Population-sharing technology is designed to collaboratively exchange information among different populations to address the lack of diversity in a single population. An information-sharing three-stage differential evolution strategy is presented to reduce the search space and improve search performance by sharing good substructures among elite individuals. New mutation strategies are proposed to utilize the different substructures between two random individuals as a new community to further improve the search performance. Experiments on various projects demonstrate the superiority of the proposed algorithm. The proposed method not only has fast convergence but also provides stable and accurate modularity that is somewhat closer to expert decomposition than that of other methods.
650		4	\|a Software modularization
650		4	\|a Multi-objective optimization
650		4	\|a Differential evolution algorithm
650		4	\|a Software architecture
650		4	\|a Software understanding
650		4	\|a Software system maintenance
700	1		\|a Xie, Wentao \|e verfasserin \|4 aut
700	1		\|a Wang, Xiaopeng \|e verfasserin \|4 aut
700	1		\|a Wang, Haining \|e verfasserin \|0 (orcid)0000-0002-5281-6090 \|4 aut
700	1		\|a Wang, Xinchao \|e verfasserin \|4 aut
700	1		\|a Li, Jinyuan \|e verfasserin \|4 aut
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Indexfelder

author_variant	y k yk w x wx x w xw h w hw x w xw j l jl
matchkey_str	kangyanxiewentaowangxiaopengwanghainingw:2023----:oidaolbrtvmlibetvifrainhrndagrt
hierarchy_sort_str	2023
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publishDate	2023
allfields	10.1016/j.eswa.2023.120207 doi (DE-627)ELV009789308 (ELSEVIER)S0957-4174(23)00709-1 DE-627 ger DE-627 rda eng 004 VZ 54.72 bkl Kang, Yan verfasserin (orcid)0000-0001-6969-0562 aut MOPISDE: A collaborative multi-objective information-sharing DE algorithm for software clustering 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The software module clustering problem (SMCP) aims to improve the internal quality of software while helping software engineers understand the system architecture and facilitating software system maintenance. However, most current methods ignore modular stability in software evolution and the topological properties of the software architecture and hence obtain decompositions much worse than the expert. Therefore, we propose a collaborative multi-objective information-sharing differential evolution (MOPISDE) algorithm for SMCP with global stability and path complexity as two new objective functions. Specifically, two new concepts are defined for SMCP as two objectives of populations and modular quality (MQ) as an objective of the third population. Population-sharing technology is designed to collaboratively exchange information among different populations to address the lack of diversity in a single population. An information-sharing three-stage differential evolution strategy is presented to reduce the search space and improve search performance by sharing good substructures among elite individuals. New mutation strategies are proposed to utilize the different substructures between two random individuals as a new community to further improve the search performance. Experiments on various projects demonstrate the superiority of the proposed algorithm. The proposed method not only has fast convergence but also provides stable and accurate modularity that is somewhat closer to expert decomposition than that of other methods. Software modularization Multi-objective optimization Differential evolution algorithm Software architecture Software understanding Software system maintenance Xie, Wentao verfasserin aut Wang, Xiaopeng verfasserin aut Wang, Haining verfasserin (orcid)0000-0002-5281-6090 aut Wang, Xinchao verfasserin aut Li, Jinyuan verfasserin aut Enthalten in Expert systems with applications Amsterdam [u.a.] : Elsevier Science, 1990 226 Online-Ressource (DE-627)320577961 (DE-600)2017237-0 (DE-576)11481807X nnns volume:226 GBV_USEFLAG_U GBV_ELV SYSFLAG_U 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_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_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 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_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 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_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.72 Künstliche Intelligenz VZ AR 226
spelling	10.1016/j.eswa.2023.120207 doi (DE-627)ELV009789308 (ELSEVIER)S0957-4174(23)00709-1 DE-627 ger DE-627 rda eng 004 VZ 54.72 bkl Kang, Yan verfasserin (orcid)0000-0001-6969-0562 aut MOPISDE: A collaborative multi-objective information-sharing DE algorithm for software clustering 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The software module clustering problem (SMCP) aims to improve the internal quality of software while helping software engineers understand the system architecture and facilitating software system maintenance. However, most current methods ignore modular stability in software evolution and the topological properties of the software architecture and hence obtain decompositions much worse than the expert. Therefore, we propose a collaborative multi-objective information-sharing differential evolution (MOPISDE) algorithm for SMCP with global stability and path complexity as two new objective functions. Specifically, two new concepts are defined for SMCP as two objectives of populations and modular quality (MQ) as an objective of the third population. Population-sharing technology is designed to collaboratively exchange information among different populations to address the lack of diversity in a single population. An information-sharing three-stage differential evolution strategy is presented to reduce the search space and improve search performance by sharing good substructures among elite individuals. New mutation strategies are proposed to utilize the different substructures between two random individuals as a new community to further improve the search performance. Experiments on various projects demonstrate the superiority of the proposed algorithm. The proposed method not only has fast convergence but also provides stable and accurate modularity that is somewhat closer to expert decomposition than that of other methods. Software modularization Multi-objective optimization Differential evolution algorithm Software architecture Software understanding Software system maintenance Xie, Wentao verfasserin aut Wang, Xiaopeng verfasserin aut Wang, Haining verfasserin (orcid)0000-0002-5281-6090 aut Wang, Xinchao verfasserin aut Li, Jinyuan verfasserin aut Enthalten in Expert systems with applications Amsterdam [u.a.] : Elsevier Science, 1990 226 Online-Ressource (DE-627)320577961 (DE-600)2017237-0 (DE-576)11481807X nnns volume:226 GBV_USEFLAG_U GBV_ELV SYSFLAG_U 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_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_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 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_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 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_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.72 Künstliche Intelligenz VZ AR 226
allfields_unstemmed	10.1016/j.eswa.2023.120207 doi (DE-627)ELV009789308 (ELSEVIER)S0957-4174(23)00709-1 DE-627 ger DE-627 rda eng 004 VZ 54.72 bkl Kang, Yan verfasserin (orcid)0000-0001-6969-0562 aut MOPISDE: A collaborative multi-objective information-sharing DE algorithm for software clustering 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The software module clustering problem (SMCP) aims to improve the internal quality of software while helping software engineers understand the system architecture and facilitating software system maintenance. However, most current methods ignore modular stability in software evolution and the topological properties of the software architecture and hence obtain decompositions much worse than the expert. Therefore, we propose a collaborative multi-objective information-sharing differential evolution (MOPISDE) algorithm for SMCP with global stability and path complexity as two new objective functions. Specifically, two new concepts are defined for SMCP as two objectives of populations and modular quality (MQ) as an objective of the third population. Population-sharing technology is designed to collaboratively exchange information among different populations to address the lack of diversity in a single population. An information-sharing three-stage differential evolution strategy is presented to reduce the search space and improve search performance by sharing good substructures among elite individuals. New mutation strategies are proposed to utilize the different substructures between two random individuals as a new community to further improve the search performance. Experiments on various projects demonstrate the superiority of the proposed algorithm. The proposed method not only has fast convergence but also provides stable and accurate modularity that is somewhat closer to expert decomposition than that of other methods. Software modularization Multi-objective optimization Differential evolution algorithm Software architecture Software understanding Software system maintenance Xie, Wentao verfasserin aut Wang, Xiaopeng verfasserin aut Wang, Haining verfasserin (orcid)0000-0002-5281-6090 aut Wang, Xinchao verfasserin aut Li, Jinyuan verfasserin aut Enthalten in Expert systems with applications Amsterdam [u.a.] : Elsevier Science, 1990 226 Online-Ressource (DE-627)320577961 (DE-600)2017237-0 (DE-576)11481807X nnns volume:226 GBV_USEFLAG_U GBV_ELV SYSFLAG_U 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_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_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 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_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 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_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.72 Künstliche Intelligenz VZ AR 226
allfieldsGer	10.1016/j.eswa.2023.120207 doi (DE-627)ELV009789308 (ELSEVIER)S0957-4174(23)00709-1 DE-627 ger DE-627 rda eng 004 VZ 54.72 bkl Kang, Yan verfasserin (orcid)0000-0001-6969-0562 aut MOPISDE: A collaborative multi-objective information-sharing DE algorithm for software clustering 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The software module clustering problem (SMCP) aims to improve the internal quality of software while helping software engineers understand the system architecture and facilitating software system maintenance. However, most current methods ignore modular stability in software evolution and the topological properties of the software architecture and hence obtain decompositions much worse than the expert. Therefore, we propose a collaborative multi-objective information-sharing differential evolution (MOPISDE) algorithm for SMCP with global stability and path complexity as two new objective functions. Specifically, two new concepts are defined for SMCP as two objectives of populations and modular quality (MQ) as an objective of the third population. Population-sharing technology is designed to collaboratively exchange information among different populations to address the lack of diversity in a single population. An information-sharing three-stage differential evolution strategy is presented to reduce the search space and improve search performance by sharing good substructures among elite individuals. New mutation strategies are proposed to utilize the different substructures between two random individuals as a new community to further improve the search performance. Experiments on various projects demonstrate the superiority of the proposed algorithm. The proposed method not only has fast convergence but also provides stable and accurate modularity that is somewhat closer to expert decomposition than that of other methods. Software modularization Multi-objective optimization Differential evolution algorithm Software architecture Software understanding Software system maintenance Xie, Wentao verfasserin aut Wang, Xiaopeng verfasserin aut Wang, Haining verfasserin (orcid)0000-0002-5281-6090 aut Wang, Xinchao verfasserin aut Li, Jinyuan verfasserin aut Enthalten in Expert systems with applications Amsterdam [u.a.] : Elsevier Science, 1990 226 Online-Ressource (DE-627)320577961 (DE-600)2017237-0 (DE-576)11481807X nnns volume:226 GBV_USEFLAG_U GBV_ELV SYSFLAG_U 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_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_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 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_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 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_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.72 Künstliche Intelligenz VZ AR 226
allfieldsSound	10.1016/j.eswa.2023.120207 doi (DE-627)ELV009789308 (ELSEVIER)S0957-4174(23)00709-1 DE-627 ger DE-627 rda eng 004 VZ 54.72 bkl Kang, Yan verfasserin (orcid)0000-0001-6969-0562 aut MOPISDE: A collaborative multi-objective information-sharing DE algorithm for software clustering 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The software module clustering problem (SMCP) aims to improve the internal quality of software while helping software engineers understand the system architecture and facilitating software system maintenance. However, most current methods ignore modular stability in software evolution and the topological properties of the software architecture and hence obtain decompositions much worse than the expert. Therefore, we propose a collaborative multi-objective information-sharing differential evolution (MOPISDE) algorithm for SMCP with global stability and path complexity as two new objective functions. Specifically, two new concepts are defined for SMCP as two objectives of populations and modular quality (MQ) as an objective of the third population. Population-sharing technology is designed to collaboratively exchange information among different populations to address the lack of diversity in a single population. An information-sharing three-stage differential evolution strategy is presented to reduce the search space and improve search performance by sharing good substructures among elite individuals. New mutation strategies are proposed to utilize the different substructures between two random individuals as a new community to further improve the search performance. Experiments on various projects demonstrate the superiority of the proposed algorithm. The proposed method not only has fast convergence but also provides stable and accurate modularity that is somewhat closer to expert decomposition than that of other methods. Software modularization Multi-objective optimization Differential evolution algorithm Software architecture Software understanding Software system maintenance Xie, Wentao verfasserin aut Wang, Xiaopeng verfasserin aut Wang, Haining verfasserin (orcid)0000-0002-5281-6090 aut Wang, Xinchao verfasserin aut Li, Jinyuan verfasserin aut Enthalten in Expert systems with applications Amsterdam [u.a.] : Elsevier Science, 1990 226 Online-Ressource (DE-627)320577961 (DE-600)2017237-0 (DE-576)11481807X nnns volume:226 GBV_USEFLAG_U GBV_ELV SYSFLAG_U 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_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_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 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_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 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_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.72 Künstliche Intelligenz VZ AR 226
language	English
source	Enthalten in Expert systems with applications 226 volume:226
sourceStr	Enthalten in Expert systems with applications 226 volume:226
format_phy_str_mv	Article
bklname	Künstliche Intelligenz
institution	findex.gbv.de
topic_facet	Software modularization Multi-objective optimization Differential evolution algorithm Software architecture Software understanding Software system maintenance
dewey-raw	004
isfreeaccess_bool	false
container_title	Expert systems with applications
authorswithroles_txt_mv	Kang, Yan @@aut@@ Xie, Wentao @@aut@@ Wang, Xiaopeng @@aut@@ Wang, Haining @@aut@@ Wang, Xinchao @@aut@@ Li, Jinyuan @@aut@@
publishDateDaySort_date	2023-01-01T00:00:00Z
hierarchy_top_id	320577961
dewey-sort	14
id	ELV009789308
language_de	englisch
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author	Kang, Yan
spellingShingle	Kang, Yan ddc 004 bkl 54.72 misc Software modularization misc Multi-objective optimization misc Differential evolution algorithm misc Software architecture misc Software understanding misc Software system maintenance MOPISDE: A collaborative multi-objective information-sharing DE algorithm for software clustering
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topic_title	004 VZ 54.72 bkl MOPISDE: A collaborative multi-objective information-sharing DE algorithm for software clustering Software modularization Multi-objective optimization Differential evolution algorithm Software architecture Software understanding Software system maintenance
topic	ddc 004 bkl 54.72 misc Software modularization misc Multi-objective optimization misc Differential evolution algorithm misc Software architecture misc Software understanding misc Software system maintenance
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title	MOPISDE: A collaborative multi-objective information-sharing DE algorithm for software clustering
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title_full	MOPISDE: A collaborative multi-objective information-sharing DE algorithm for software clustering
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title_sort	mopisde: a collaborative multi-objective information-sharing de algorithm for software clustering
title_auth	MOPISDE: A collaborative multi-objective information-sharing DE algorithm for software clustering
abstract	The software module clustering problem (SMCP) aims to improve the internal quality of software while helping software engineers understand the system architecture and facilitating software system maintenance. However, most current methods ignore modular stability in software evolution and the topological properties of the software architecture and hence obtain decompositions much worse than the expert. Therefore, we propose a collaborative multi-objective information-sharing differential evolution (MOPISDE) algorithm for SMCP with global stability and path complexity as two new objective functions. Specifically, two new concepts are defined for SMCP as two objectives of populations and modular quality (MQ) as an objective of the third population. Population-sharing technology is designed to collaboratively exchange information among different populations to address the lack of diversity in a single population. An information-sharing three-stage differential evolution strategy is presented to reduce the search space and improve search performance by sharing good substructures among elite individuals. New mutation strategies are proposed to utilize the different substructures between two random individuals as a new community to further improve the search performance. Experiments on various projects demonstrate the superiority of the proposed algorithm. The proposed method not only has fast convergence but also provides stable and accurate modularity that is somewhat closer to expert decomposition than that of other methods.
abstractGer	The software module clustering problem (SMCP) aims to improve the internal quality of software while helping software engineers understand the system architecture and facilitating software system maintenance. However, most current methods ignore modular stability in software evolution and the topological properties of the software architecture and hence obtain decompositions much worse than the expert. Therefore, we propose a collaborative multi-objective information-sharing differential evolution (MOPISDE) algorithm for SMCP with global stability and path complexity as two new objective functions. Specifically, two new concepts are defined for SMCP as two objectives of populations and modular quality (MQ) as an objective of the third population. Population-sharing technology is designed to collaboratively exchange information among different populations to address the lack of diversity in a single population. An information-sharing three-stage differential evolution strategy is presented to reduce the search space and improve search performance by sharing good substructures among elite individuals. New mutation strategies are proposed to utilize the different substructures between two random individuals as a new community to further improve the search performance. Experiments on various projects demonstrate the superiority of the proposed algorithm. The proposed method not only has fast convergence but also provides stable and accurate modularity that is somewhat closer to expert decomposition than that of other methods.
abstract_unstemmed	The software module clustering problem (SMCP) aims to improve the internal quality of software while helping software engineers understand the system architecture and facilitating software system maintenance. However, most current methods ignore modular stability in software evolution and the topological properties of the software architecture and hence obtain decompositions much worse than the expert. Therefore, we propose a collaborative multi-objective information-sharing differential evolution (MOPISDE) algorithm for SMCP with global stability and path complexity as two new objective functions. Specifically, two new concepts are defined for SMCP as two objectives of populations and modular quality (MQ) as an objective of the third population. Population-sharing technology is designed to collaboratively exchange information among different populations to address the lack of diversity in a single population. An information-sharing three-stage differential evolution strategy is presented to reduce the search space and improve search performance by sharing good substructures among elite individuals. New mutation strategies are proposed to utilize the different substructures between two random individuals as a new community to further improve the search performance. Experiments on various projects demonstrate the superiority of the proposed algorithm. The proposed method not only has fast convergence but also provides stable and accurate modularity that is somewhat closer to expert decomposition than that of other methods.
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title_short	MOPISDE: A collaborative multi-objective information-sharing DE algorithm for software clustering
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author2	Xie, Wentao Wang, Xiaopeng Wang, Haining Wang, Xinchao Li, Jinyuan
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up_date	2024-07-07T00:21:39.231Z
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MOPISDE: A collaborative multi-objective information-sharing DE algorithm for software clustering

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