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An orthogonal approach to reusable component discovery in cloud migration
Zhao J.2; Zhou J.2; Yang H.3; Liu G.1
2015
Source PublicationChina Communications
ISSN16735447
Volume12Issue:5Pages:134-151
Abstract

As an innovative software application mode, Software as a service (SaaS) shows many attractive advantages. Migrating legacy system to SaaS can make outdated systems revived. In the process of migration, the existing valuable components need to be discovered and reused in order that the target system could be developed/integrated more efficiently. An innovative approach is proposed in this paper to extract the reusable components from legacy systems. Firstly, implementation models of legacy system are recovered through reverse engineering. Secondly, function models are derived by vertical clustering, and then logical components are discovered by horizontal clustering based on the function models. Finally, the reusable components with specific feature descriptions are extracted. Through experimental verification, the approach is considered to be efficient in reusable component discovery and to be helpful to migrating legacy system to SaaS.

KeywordArchitecture Recovery Horizontal Clustering Legacy System Reusable Component Vertical Clustering
DOI10.1109/CC.2015.7112036
URLView the original
Language英語English
WOS IDWOS:000354386100015
Scopus ID2-s2.0-84930751561
Fulltext Access
Citation statistics
Document TypeJournal article
CollectionUniversity of Macau
Affiliation1.University of South Wales
2.Inner Mongolia University China
3.Bath Spa University
Recommended Citation
GB/T 7714
Zhao J.,Zhou J.,Yang H.,et al. An orthogonal approach to reusable component discovery in cloud migration[J]. China Communications, 2015, 12(5), 134-151.
APA Zhao J.., Zhou J.., Yang H.., & Liu G. (2015). An orthogonal approach to reusable component discovery in cloud migration. China Communications, 12(5), 134-151.
MLA Zhao J.,et al."An orthogonal approach to reusable component discovery in cloud migration".China Communications 12.5(2015):134-151.
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