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IEEE 38th Annual International Computers, Software and Applications Conference (COMPSAC)

作者： Colanzi, Thelma Elita Vergilio, Silvia Regina Univ Fed Parana Dept Comp Sci BR-19031970 Curitiba Parana Brazil Univ Estadual Maringa Dept Comp Sci BR-87020900 Maringa Parana Brazil

ISBN: (纸本)9781479935741

The product line architecture (PLA) design is a multi-objective optimization problem that can be properly solved in the Search Based Software Engineering (SBSE) field. However, the PLA design has specific characteristics. For example, the PLA is designed in terms of features and a highly modular PLA is necessary to enable the growth of a software product line. However, existing search based design approaches do not consider such needs. To overcome this limitation, this paper introduces a feature-driven crossover operator that aims at improving feature modularization. The proposed operator was applied in an empirical study using the multi-objective evolutionary algorithm named NSGAII. In comparison with another version of NSGAII that uses only mutation operators, the feature-driven crossover version found a greater diversity of solutions (potential PLA designs), with higher feature-based cohesion, and less feature scattering and tangling.

关键词： product line architecture design Multi-objective Evolutionary Algorithms Crossover Operator

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A Comparative Analysis of Two Multi-Objective Evolutionary Algorithms in product line architecture design Optimization 26

A Comparative Analysis of Two Multi-Objective Evolutionary A...

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26th IEEE International Conference on Tools with Artificial Intelligence (ICTAI)

作者： Colanzi, Thelma Elita Vergilio, Silvia Regina State Univ Maringa UEM Dept Comp Sci Maringa Parana Brazil Fed Univ Parana UFPR Dept Comp Sci Curitiba Parana Brazil

ISBN: (纸本)9781479965731

The product line architecture (PLA) design is a multi-objective optimization problem that can be properly solved with search-based algorithms. However, search-based PLA design is an incipient research field. Due to this, works in this field have addressed main points to solve the problem: adequate representation, specific search operators and suitable evaluation fitness functions. Similarly what happens in the search-based design of traditional software, existing works on search-based PLA design use NSGA-II, without evaluating the characteristics of this algorithm, such as the use of crossover operator. Considering this fact, this paper reports results from a comparative analysis of two algorithms, NSGA-II and PAES, to the PLA design problem. PAES was chosen because it implements a different evolution strategy that does not employ crossover. An experimental study was carried out with nine PLAs and results of the conducted study attest that NSGA-II performs better than PAES in the PLA design context.

关键词： software product line multi-objective algorithms product line architecture design

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A feature-driven crossover operator for multi-objective and evolutionary optimization of product line architectures

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JOURNAL OF SYSTEMS AND SOFTWARE 2016年 121卷 126-143页

作者： Colanzi, Thelma Elita Vergilio, Silvia Regina State Univ Maringa UEM Informat Dept DIN BR-87020900 Maringa Parana Brazil Fed Univ Parana UFPR Comp Sci Dept DInf BR-81531970 Curitiba Parana Brazil

The optimization of a product line architecture (PLA) design can be modeled as a multi-objective problem, influenced by many factors, such as feature modularization, extensibility and other design principles. Due to this it has been properly solved in the Search Based Software Engineering (SBSE) field. However, previous empirical studies optimized PLA design using the multi-objective and evolutionary algorithm NSGA-II, without applying one of the most important genetic operators: the crossover. To overcome this limitation, this paper presents a feature-driven crossover operator that aims at improving feature modularization in PLA design. The proposed operator was applied in two empirical studies using NSGA-II in comparison with another version of NSGA-II that uses only mutation operators. The results show the usefulness and applicability of the proposed operator. The NSGA-II version that applies the feature-driven crossover found a greater diversity of solutions (potential PLA designs), with higher feature-based cohesion, and less feature scattering and tangling. (C) 2016 Elsevier Inc. All rights reserved.

关键词： product line architecture design Multi-objective genetic algorithm Crossover operator Empirical study

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