A Novel Hybrid ABC-PSO Algorithm for Effort Estimation of Software Projects Using Agile Methodologies

Khuat, TT; Le, MH

A Novel Hybrid ABC-PSO Algorithm for Effort Estimation of Software Projects Using Agile Methodologies

Khuat, TT Le, MH

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Publisher:: Walter de Gruyter GmbH
Publication Type:: Journal Article
Citation:: Journal of Intelligent Systems, 2018, 27, (3), pp. 489-506
Issue Date:: 2018-07-26

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Field	Value	Language
dc.contributor.author	Khuat, TT
dc.contributor.author	Le, MH
dc.date.accessioned	2022-09-03T21:45:55Z
dc.date.available	2022-09-03T21:45:55Z
dc.date.issued	2018-07-26
dc.identifier.citation	Journal of Intelligent Systems, 2018, 27, (3), pp. 489-506
dc.identifier.issn	0334-1860
dc.identifier.issn	2191-026X
dc.identifier.uri	http://hdl.handle.net/10453/161292
dc.description.abstract	In modern software development processes, software effort estimation plays a crucial role. The success or failure of projects depends greatly on the accuracy of effort estimation and schedule results. Many studies focused on proposing novel models to enhance the accuracy of predicted results; however, the question of accurate estimation of effort has been a challenging issue with regards to researchers and practitioners, especially when it comes to projects using agile methodologies. This study aims at introducing a novel formula based on team velocity and story point factors. The parameters of this formula are then optimized by employing swarm optimization algorithms. We also propose an improved algorithm combining the advantages of the artificial bee colony and particle swarm optimization algorithms. The experimental results indicated that our approaches outperformed methods in other studies in terms of the accuracy of predicted results.
dc.language	en
dc.publisher	Walter de Gruyter GmbH
dc.relation.ispartof	Journal of Intelligent Systems
dc.relation.isbasedon	10.1515/jisys-2016-0294
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0801 Artificial Intelligence and Image Processing, 0806 Information Systems, 1702 Cognitive Sciences
dc.subject.classification	Artificial Intelligence & Image Processing
dc.title	A Novel Hybrid ABC-PSO Algorithm for Effort Estimation of Software Projects Using Agile Methodologies
dc.type	Journal Article
utslib.citation.volume	27
utslib.for	0801 Artificial Intelligence and Image Processing
utslib.for	0806 Information Systems
utslib.for	1702 Cognitive Sciences
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - AAI - Advanced Analytics Institute Research Centre
utslib.copyright.status	closed_access	*
dc.date.updated	2022-09-03T21:45:54Z
pubs.issue	3
pubs.publication-status	Published
pubs.volume	27
utslib.citation.issue	3

Abstract:

In modern software development processes, software effort estimation plays a crucial role. The success or failure of projects depends greatly on the accuracy of effort estimation and schedule results. Many studies focused on proposing novel models to enhance the accuracy of predicted results; however, the question of accurate estimation of effort has been a challenging issue with regards to researchers and practitioners, especially when it comes to projects using agile methodologies. This study aims at introducing a novel formula based on team velocity and story point factors. The parameters of this formula are then optimized by employing swarm optimization algorithms. We also propose an improved algorithm combining the advantages of the artificial bee colony and particle swarm optimization algorithms. The experimental results indicated that our approaches outperformed methods in other studies in terms of the accuracy of predicted results.

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http://hdl.handle.net/10453/161292