Automating identification of services and their variability for product lines using NSGA-II

doi:10.1007/s11704-016-5121-6

Front. Comput. Sci.

2017, Vol. 11

Issue (3) : 444-464 https://doi.org/10.1007/s11704-016-5121-6

RESEARCH ARTICLE

Automating identification of services and their variability for product lines using NSGA-II

Sedigheh KHOSHNEVIS(

), Fereidoon SHAMS

Software Engineering Department, Shahid Beheshti University, Tehran 19836, Iran

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Abstract

Architecture-level business services are identified based on business processes; and likewise, in serviceoriented product lines, identifying the domain architecturelevel business services and their variability is preferred to be based on business processes and their variability. Identification of business services for a product line satisfying a set of given design metrics (such as cohesion and coupling) is extremely difficult for a domain architect, since there are many product configurations for which the services must be proper at the same time. This means that the identified services must have proper values for n metrics in m different configurations at the same time. The problem becomes more serious when there are high degrees of variability and complexity embedded in the business processes that are the basis for service identification.We contribute to solve the multi-objective optimization problem of identifying business services for a product line by partitioning the graph of a business process variability model utilizing Non-dominated Sorting Genetic Algorithm-II. The service specification is achieved based on the results of the partitioning. The variability of the services is then determined in terms of mandatory and optional services as well as variability relationships, which are all represented in a Service Variability Model. The method was empirically evaluated through experimentation, and showed proper levels of reusability and variability. Furthermore, the resulting models were fully consistent.

Keywords software product line service variability model business process genetic algorithm

Corresponding Author(s): Sedigheh KHOSHNEVIS

Just Accepted Date: 16 December 2015 Online First Date: 14 September 2016 Issue Date: 25 May 2017

Cite this article:

Sedigheh KHOSHNEVIS,Fereidoon SHAMS. Automating identification of services and their variability for product lines using NSGA-II[J]. Front. Comput. Sci., 2017, 11(3): 444-464.

URL:

https://academic.hep.com.cn/fcs/EN/10.1007/s11704-016-5121-6
https://academic.hep.com.cn/fcs/EN/Y2017/V11/I3/444

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