Alternatives Selection Using GORE Based on Fuzzy Numbers and TOPSIS
- 1 Software Architectures and Product Line Group, Ilmenau University of Technology, Ilmenau, Germany
- 2 Software Architectures and Product Line Group, Ilmenau University of Technology, Ilmenau, Germany
- 3 Software Architectures and Product Line Group, Ilmenau University of Technology, Ilmenau, Germany
Abstract
Context and Motivation: The notion of goal and goal models is ideal for the alternative systems. Goal models provide us different alternatives during goal oriented requirements engineering. Question/Problem: Once we find different alternatives, we need to evaluate these alternatives to select the best one. Ideas: The selection process consists of two main parts. In first part of the selection process among alternatives, we will use techniques in which we establish some evaluation criteria. The evaluation criteria are based on leaf level goals. Stakeholders are involved to contribute their opinions about the evaluation criteria. The input provided by various stakeholders is then converted into quantifiable numbers using fuzzy triangle numbers. After applying the defuzzification process on fuzzy triangle numbers we get scores (weights) for each criteria. In second part, these scores are used in the selection process to select the best alternative. Contribution: The two steps selection process helps us to select the best alternative among many alternatives. We have described the process and applied it to “cyclecomputer” selection case study.
- Erturul, R. and Karakaolu, N. (2008) Comparison of Fuzzy Ahp and Fuzzy Topsis Methods for Facility Location Selection. The International Journal of Advanced Manufacturing Technology, 39, 783-795.
- Chen, C.-T. (2000) Extensions of the Topsis for Group Decision-Making under Fuzzy Environment. Fuzzy Sets and Systems, 114, 1-9.
- Lapouchnian, A. (2005) Goal-Oriented Requirement Engineering. An Overview of the Current Res-earch, Technical Report, University of Toronto, Toronto.
- Darimont, R. and van Lamsweerde, A. (1996) Formal Refinement Patterns for Goal-Driven Requir-ements Elaboration. Proceedings of the 4th ACM Symposium on the Foundation of Software Eng-ineering, San Francisco, October 1996, 179-190.
- van Lamsweerde, A. (2000) Requirements Engineering in the Year 00: A Research Perspective. Proceedings of the 2000 International Conference on Software Engineering, Limerick, 4-11 June 2000, 5-19.
- Zave, P. (1997) Classi Cation of Research Eorts in Requirements Engineering. ACM Computing Surveys, 29, 315-321.
- Anton, A.I. (1996) Goal-Based Requirements Analysis. Proceedings of the 2nd International Conference on Requirements Engineering, Colorado Springs, 15-18 April 1996, 136-144. http://dx.doi.org/10.1109/icre.1996.491438
- Mohammad Dabbagh, S.P.L. (2014) An Approach for Integrating the Prioritization of Functional and Nonfunctional Requirements. The Scientic World Journal, 2014, Article ID: 737626.
- Goli, D. (2013) Group Fuzzy Topsis Methodology in Computer Security Software Selection. International Journal of Fuzzy Logic Systems, 3, 29.
- Olson, D.L. (2004) Comparison of Weights in Topsis Models. Mathematical and Computer Modelling, 40, 721-727.
- Mansoor, A. and Streitferdt, D. (2011) On the Impact of Goals on Long-Living Systems. Workshop: Evolutionäre Software-und Systementwicklung-Methoden und Erfahrungen, Karlsruhe, February 2011, 133-138.
- Liou, T.-S. and Wang, M.-J. (1992) Ranking Fuzzy Numbers with Integral Value. Fuzzy Sets and Systems, 50, 247- 255.
- Saaty, T.L. (2008) Decision Making with the Analytic Hierarchy Process. International Journal of Services Sciences, 1, 83-98.
- Wang, T.-C. and Lee, H.-D. (2009) Developing a Fuzzy Topsis Approach Based on Subjective Weights and Objective Weights. Expert Systems with Applications, 36, 8980-8985.