- 期刊
- OpenAccess
Empirical Evaluation of Fuzzy Synthetic Based Framework for Multifaceted Component Classification and Selection
並列摘要
Component Based Software Engineering (CBSE) provides an approach to develop high quality software system at less cost by using fresh and existing software components. The quality of the software system is based on the quality of individual software component integrated. Application developer wants the good or the fittest component to assemble and improve the quality of the software product. The application developer specifies the criteria and requirements of software systems and uses them in selecting the fit components. Component classification and selection is a practical problem and requires complete and predictable input information. It is missing due to uncertainty in judgment and impression in calculations. Hence, component fitness evaluation, classification and selection are critical, multi-faceted, fuzzy and vague nature problems. There exists many component selection approaches, but theses lack the repeatable, usable, exile, multi-faceted and automated processes for component selection and filtration. These approaches are not fulfilling the objectives of software industry in terms of cost, quality and precision. So, there is need of hour to devise an intelligent approach for multifaceted component fitness evaluation, classification and selection. In this study, fuzzy synthetic based approach is proposed for multi-criteria fitness evaluation, classification and selection of software component. For validation of the proposed framework, fifteen black box components of calculators are used. It helps the application developer in selecting fit or high quality component. The proposed framework reduces the cost and enhances the quality, productivity of software systems.
延伸閱讀
- Ju, Z. J., Liu, H. H., & Xiong, Y. L. (2014). Fuzzy Empirical Copula for Estimating Data Dependence Structure. International Journal of Fuzzy Systems, 16(2), 160-172. https://www.airitilibrary.com/Article/Detail?DocID=15622479-201406-201411040017-201411040017-160-172
- Sahoo, A., & Dash, J. K. (2016). A Novel Method for Optimal Solution of Fuzzy Chance Constraint Single-Period Inventory Model. Advances in Fuzzy Systems, 2016(), 179-188. https://doi.org/10.1155/2016/6132768
- Huang, C. F., Chang, B. R., Cheng, D. W., & Chang, C. H. (2012). Feature Selection and Parameter Optimization of a Fuzzy-based Stock Selection Model Using Genetic Algorithms. International Journal of Fuzzy Systems, 14(1), 65-75. https://doi.org/10.30000/IJFS.201203.0007
- Wu, Y. (2006). An empirical study of the use of conceptual models for mutation testing of database application programs [master's thesis, The University of Hong Kong]. Airiti Library. https://www.airitilibrary.com/Article/Detail?DocID=U0029-1812201200012930
- Kuzhali, J. V., & Vengataasalam, S. (2014). A Novel Ensemble Classifier based Classification on Large Datasets with Hybrid Feature Selection Approach. Research Journal of Applied Sciences, Engineering and Technology, 7(17), 3633-3642. https://www.airitilibrary.com/Article/Detail?DocID=20407467-201405-201507070018-201507070018-3633-3642