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Nowadays, product line, as an important topic in software development domain, has been paid attention by software engineering practitioners. Finding suitable components to construct an efficient and comprehensive product line with low costs is one of the critical problems in this domain which demands to be satisfied. In this paper a method is presented which can manage and control complexities of component selection problem, an NP problem, and result in creation of declared product line.By making use of this method, a product line will be constructed with suitably-adopted components to cover up requirements based on architecture; that's why software product line development process will enhance in reducing risks and costs of development.

Today's software systems are more frequently composed from preexisting commercial or non-commercial components and connectors. These components provide complex and independent functionality and are engaged in complex interactions. Component-Based Software Engineering (CBSE) is concerned with composing, selecting and designing such components. As the popularity of this approach and hence number of commercially available software components grows, selecting a set of components to satisfy a set of requirements while minimizing cost is becoming more difficult. This problem necessitates the design of efficient algorithms to automate component selection for software developing organizations. We address this challenge through analysis of Component Selection, the NP-complete process of selecting a minimal cost set of components to satisfy a set of objectives. Due to the high order of computational complexity of this problem, we examine approximating solutions that make the component selection process practicable. We adapt a greedy approach and a genetic algorithm to approximate this problem. We examined the performance of studied algorithms on a set of selected ActiveX components. Comparing the results of these two algorithms with the choices made by a group of human experts shows that we obtain better results using these approximation algorithms.