Ranking of Software Effort Estimation Selection Criteria Based On Fuzzy Set Theory

Aparna Jindal; Rakesh Garg; Ashu Bansal

doi:https://doi.org/10.51976/ijari.411609

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Ranking of Software Effort Estimation Selection Criteria Based On Fuzzy Set Theory

Aparna Jindal, Rakesh Garg, Ashu Bansal

Vol 4 , Issue 1 , January - March 2016 | Pages: 57-62 | Research Paper

https://doi.org/10.51976/ijari.411609

Received: January 25, 2016 | Revised: February 20, 2016 | Accepted: February 28, 2016 | Published Online: March 15, 2016

Author Details ( * ) denotes Corresponding author

1. * Aparna Jindal, Department of Computer Science & Engineering, Hindu College of Engineering,, Sonepat, Haryana, India (jindalaparna5@gmail.com)

2. Rakesh Garg, Department of Computer Science & Engineering, Hindu College of Engineering, Sonepat, Haryana, India

3. Ashu Bansal, Department of Computer Science & Engineering, Hindu College of Engineering, Sonepat, Haryana, India

The selection and evaluation of Software effort estimation models has always been a challenging task for the software developers and the project managers. A lot of research has been done by various researchers on this by considering it as multi-criteria decision making problem. So, a better understanding of various selection criteria and their importance in this regard is required. In this paper, first the identification of the various software effort estimation model selection criteria is done, then by applying fuzzy set theory the local and global weights of these selection criteria are calculated and finally the selection criteria are ranked according to their global weights showing the importance of each criterion.

Keywords

Selection Criteria; Fuzzy-Set Theory.

Menzies, T., Chen, Z., Hihn, J., & Lum, K. (2006). Selecting best practices for effort estimation. Software Engineering, IEEE Transactions on, 32(11), 883-895.
Basha, S., & Ponnurangam, D. (2010). Analysis of empirical software effort estimation models. arxiv preprint arxiv:1004.1239.
Kaur, J., Singh, S., Kahlon, K. S., & Bassi, P. (2010). Neural network-a novel technique for software effort estimation. International Journal of Computer Theory and Engineering, 2(1), 1793-8201
Sehra, S. K., Brar, D., Singh, Y., & Kaur, D. (2013). Multi criteria decision making approach for selecting effort estimation model. arXiv preprint arXiv:1310.5220.
Garcia-Diaz, N., Lopez-Martin, C., & Chavoya, A. (2013). A comparative study of two fuzzy logic models for software development effort estimation. Procedia Technology, 7, 305-314.
Eberendu, A. C. Software Project Cost Estimation: Issues, Problems and Possible Solutions. International Journal of Engineering Science Invention, 3, 38-43.
Moløkken-Østvold, K., Jørgensen, M., Tanilkan, S. S., Gallis, H., Lien, A. C., & Hove, S. E. (2004, September). A survey on software estimation in the norwegian industry. In Software Metrics, 2004. Proceedings. 10th International Symposium on (pp. 208-219). IEEE.
Leung, H., & Fan, Z. (2002). Software cost estimation. Handbook of Software Engineering, Hong Kong Polytechnic University.
Kaufmann, A., & Gupta, M. M. (1988). Fuzzy mathematical models in engineering and management science. Elsevier Science Inc.
10. Dubois, D., & Prade, H. (1979). Fuzzy real algebra: some results. Fuzzy sets and systems, 2(4), 327-348.