Activity Diagram Synthesis Using Labelled Graphs And the Genetic Algorithm

Wang, C; Jin, Z; Zhang, W; Zowghi, D; Zhao, H; Jiao, W

Activity Diagram Synthesis Using Labelled Graphs And the Genetic Algorithm

Wang, C Jin, Z Zhang, W Zowghi, D

Zhao, H Jiao, W

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Publisher:: Springer Verlag
Publication Type:: Journal Article
Citation:: Journal of Computer Science and Technology, 2021, 33, (1), pp. 1-23
Issue Date:: 2021-01-01

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Field	Value	Language
dc.contributor.author	Wang, C
dc.contributor.author	Jin, Z
dc.contributor.author	Zhang, W
dc.contributor.author	Zowghi, D https://orcid.org/0000-0002-6051-0155
dc.contributor.author	Zhao, H
dc.contributor.author	Jiao, W
dc.date.accessioned	2021-11-13T20:21:50Z
dc.date.available	2021-11-13T20:21:50Z
dc.date.issued	2021-01-01
dc.identifier.citation	Journal of Computer Science and Technology, 2021, 33, (1), pp. 1-23
dc.identifier.issn	1000-9000
dc.identifier.uri	http://hdl.handle.net/10453/151558
dc.description.abstract	Many applications need to meet diverse requirements of a large-scale distributed user group. That challenges the current requirements engineering techniques. Crowd-based requirements engineering was proposed as an umbrella term for dealing with the requirements development in the context of large-scale user group. However, there are still many issues. Among others, a key issue is when a set of requirements descriptions from different participants are received, how to merge these requirements to produce the synthesized requirements description. Appropriate techniques are needed for supporting the requirements synthesis. Diagrams are widely used in industry to represent requirements. This paper chooses the activity diagrams and proposes a novel approach for the activity diagram synthesis which adopts genetic algorithm to repeatedly modify a population of individual solutions toward an optimal solution. As a result, it can automatically generate a resulting diagram which combines the commonalities as many as possible while leveraging the variabilities of a set of input diagrams. The approach is featured by (1) the labelled graph is proposed as the representation of the candidate solutions during the iterative evolution; (2) the generalized entropy is proposed and defined as the measurement of the solutions; (3) the genetic algorithm is designed for sorting out the high-quality solution. Four cases of different scales are used to evaluate the effectiveness of the approach. The experimental results show that not only the approach gets high precision and recall but also the resulting diagram satisfies the properties of minimization and information preservation and can support the requirements traceability
dc.language	en
dc.publisher	Springer Verlag
dc.relation	Department of Innovation, Industry, Science and Research (Previously known as DEST)CH070139
dc.relation.ispartof	Journal of Computer Science and Technology
dc.relation.isbasedon	10.1007/s11390-020-0293-9
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	08 Information and Computing Sciences
dc.subject.classification	Software Engineering
dc.title	Activity Diagram Synthesis Using Labelled Graphs And the Genetic Algorithm
dc.type	Journal Article
utslib.citation.volume	33
utslib.for	08 Information and Computing Sciences
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/DVC (Research)
pubs.organisational-group	/University of Technology Sydney/Strength - HCTD - Human Centred Technology Design
pubs.organisational-group	/University of Technology Sydney/Strength - STEM Education Futures
pubs.organisational-group	/University of Technology Sydney/DVC (Research)/Graduate Research School
utslib.copyright.status	closed_access	*
pubs.consider-herdc	true
dc.date.updated	2021-11-13T20:21:49Z
pubs.issue	1
pubs.publication-status	Published online
pubs.volume	33
utslib.citation.issue	1

Abstract:

Many applications need to meet diverse requirements of a large-scale distributed user group. That challenges the current requirements engineering techniques. Crowd-based requirements engineering was proposed as an umbrella term for dealing with the requirements development in the context of large-scale user group. However, there are still many issues. Among others, a key issue is when a set of requirements descriptions from different participants are received, how to merge these requirements to produce the synthesized requirements description. Appropriate techniques are needed for supporting the requirements synthesis. Diagrams are widely used in industry to represent requirements. This paper chooses the activity diagrams and proposes a novel approach for the activity diagram synthesis which adopts genetic algorithm to repeatedly modify a population of individual solutions toward an optimal solution. As a result, it can automatically generate a resulting diagram which combines the commonalities as many as possible while leveraging the variabilities of a set of input diagrams. The approach is featured by (1) the labelled graph is proposed as the representation of the candidate solutions during the iterative evolution; (2) the generalized entropy is proposed and defined as the measurement of the solutions; (3) the genetic algorithm is designed for sorting out the high-quality solution. Four cases of different scales are used to evaluate the effectiveness of the approach. The experimental results show that not only the approach gets high precision and recall but also the resulting diagram satisfies the properties of minimization and information preservation and can support the requirements traceability

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http://hdl.handle.net/10453/151558