Identifying software component association with genetic algorithm

Yan, X; Zhang, C; Zhang, S

Identifying software component association with genetic algorithm

Yan, X Zhang, C

Zhang, S

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Publication Type:: Journal Article
Citation:: International Journal of Software Engineering and Knowledge Engineering, 2004, 14 (4), pp. 441 - 447
Issue Date:: 2004-08-01

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Field	Value	Language
dc.contributor.author	Yan, X	en_US
dc.contributor.author	Zhang, C https://orcid.org/0000-0001-5715-7154	en_US
dc.contributor.author	Zhang, S	en_US
dc.date.issued	2004-08-01	en_US
dc.identifier.citation	International Journal of Software Engineering and Knowledge Engineering, 2004, 14 (4), pp. 441 - 447	en_US
dc.identifier.issn	0218-1940	en_US
dc.identifier.uri	http://hdl.handle.net/10453/4009
dc.description.abstract	Identifying software component association is useful for component management and component retrieval. In this paper we design an evolutionary strategy to understand software structure better and identify software component association, by using genetic algorithm. Our mining strategy is effective for global search, especially when the searched space is so large that it is hardly possible to use deterministic search method.	en_US
dc.relation	http://purl.org/au-research/grants/arc/DP0343109
dc.relation	http://purl.org/au-research/grants/arc/DP0449535
dc.relation.ispartof	International Journal of Software Engineering and Knowledge Engineering	en_US
dc.relation.isbasedon	10.1142/S0218194004001695	en_US
dc.subject.classification	Software Engineering	en_US
dc.title	Identifying software component association with genetic algorithm	en_US
dc.type	Journal Article
utslib.citation.volume	4	en_US
utslib.citation.volume	14	en_US
utslib.for	080309 Software Engineering	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
utslib.for	0803 Computer Software	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/DVC (International)
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - ACRI - Australia China Relations Institute
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
utslib.copyright.status	closed_access
pubs.issue	4	en_US
pubs.publication-status	Published	en_US
pubs.volume	14	en_US

Abstract:

Identifying software component association is useful for component management and component retrieval. In this paper we design an evolutionary strategy to understand software structure better and identify software component association, by using genetic algorithm. Our mining strategy is effective for global search, especially when the searched space is so large that it is hardly possible to use deterministic search method.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/4009