Component-based software system (CBSS) development technique is an emerging discipline that promises to take software development into a new era. As hardware systems are presently being constructed from kits of parts, software systems may also be assembled from components. It is more reliable to reuse software than to create. It is the glue code and individual components reliability that contribute to the reliability of the overall system. Every component contributes to overall system reliability according to the number of times it is being used, some components are of critical usage, known as usage frequency of component. The usage frequency decides the weight of each component. According to their weights, each component contributes to the overall reliability of the system. Therefore, ranking of components may be obtained by analyzing their reliability impacts on overall application. In this paper, we propose the application of fuzzy multi-objective optimization on the basis of ratio analysis, Fuzzy-MOORA. The method helps us find the best suitable alternative, software component, from a set of available feasible alternatives named software components. It is an accurate and easy to understand tool for solving multi-criteria decision making problems that have imprecise and vague evaluation data. By the use of ratio analysis, the proposed method determines the most suitable alternative among all possible alternatives, and dimensionless measurement will realize the job of ranking of components for estimating CBSS reliability in a non-subjective way. Finally, three case studies are shown to illustrate the use of the proposed technique.
Service selection is a very challenging core task in service oriented architecture-based application (SOABA) development. Service selection is based on user's business need and budget available. A large amount of effort is invested in selecting the most preferred service from a pool of similar services available in the market by various service providers. To propose a direction for solving the problem of service selection effort (SSE) estimation in SOABA is the objective in this study. An algorithm for SSE estimation powered by information entropy weight (IEW) fuzzy comprehensive evaluation model is presented in this study wherein the synthesis performance of each candidate service is evaluated to select the most preferred service. Larger the synthesis performance of a candidate service, higher the chances of its selection and less will be the effort invested. An empirical study is presented that assess the 24 significant parameters that affect SSE estimation by using proposed algorithm. This approach is comprehensible and rational and well suited for SOABA. The results obtained via this proposed method suggest its applicability and usefulness for real-world applications. The proposed work also explains why IEW method is useful for SSE estimation along with the research gap in existing common evaluation methods.
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