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Abbas, Asad
- Multi-Objective Optimization of Feature Model in Software Product Line: Perspectives and Challenges
Abstract Views :144 |
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Authors
Affiliations
1 Department of Computer Science and Engineering, Hanyang University, South Korea
1 Department of Computer Science and Engineering, Hanyang University, South Korea
Source
Indian Journal of Science and Technology, Vol 9, No 45 (2016), Pagination:Abstract
Software Product Line (SPL) is process for developing families of software with reusability of features categorized as common and variable features. Feature Model (FM) is developed to manage these features. Common features are easy to manage, however variable features are hard to manage because of complex relations and constraints between features. Optimization is required to manage the variabilities for best selection of features and product configurations. To this end, different Multi-Objective Evolutionary Algorithms have been proposed to get the optimal solutions of feature model. In this paper we have compared among three main optimization algorithms i.e. IBEA, NSGA-II and MOCell. Our comparison is based on previous research correctness solutions for product’s configuration with five objective functions on different feature models from SPLOT and LVAT repositories. The goal of this comparison is to find the current research prospective and challenges of multi-objective optimization in FM.Keywords
Feature Model, Optimization of Feature Model, Software Product Line.- An Approach for Optimized Feature Selection in Software Product Lines using Union-Find and Genetic Algorithms
Abstract Views :213 |
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Authors
Affiliations
1 Department of Computer Science and Engineering, Hanyang University, KP
1 Department of Computer Science and Engineering, Hanyang University, KP
Source
Indian Journal of Science and Technology, Vol 9, No 17 (2016), Pagination:Abstract
In Software Product Line (SPL), feature model is highly recommended to manage the commonalities and variability of features under resource constraints of mandatory, optional and alternative. Features with mandatory constraints and high in dependency with other features are identified as crosscutting concerns; reduce the reusability of resources. It is important to find and modularize these concerns at modeling level. With this practice, these concerns do not effect if deletion or addition is required from entire system. In this paper we have applied Union-find algorithm to find crosscutting concerns in feature model. We evaluated our approach by applying on an automobile feature model with various dependencies between features, and found required crosscutting concerns. By this approach, identification of crosscutting concerns and their modularization made easier. Further, we have also applied genetic algorithm to get optimized feature selection under cost constraint with high performance. In SPL, as crosscutting concerns are mandatory features with fix cost and performance, optimization on feature model is necessary under consideration of crosscutting concerns. Our approach found all possible products according to crosscutting concerns, cost and performance at modeling level of an automobile feature model. At last, we found all products from minimum to maximum cost with respect to least maximum performance by using GA optimization technique.Keywords
Feature Model, Genetic Algorithm, Optimization, Software Product Line, Union-find Algorithm- A Comparative Study of Multithreading APIs for Software of ICT Equipment
Abstract Views :150 |
PDF Views:0
Authors
Affiliations
1 Department of Computer Science and Engineering,Hanyang University ERICA, Ansan, South Korea, KR
1 Department of Computer Science and Engineering,Hanyang University ERICA, Ansan, South Korea, KR