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Performance Evaluation of Regression Techniques for Effort Estimation
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 52 - Number 6 |
| Year of Publication: 2012 |
| Authors: Parasana Sankara Rao, Kiran Kumar Reddi |
10.5120/8204-1601
|
Parasana Sankara Rao, Kiran Kumar Reddi . Performance Evaluation of Regression Techniques for Effort Estimation. International Journal of Computer Applications. 52, 6 ( August 2012), 8-12. DOI=10.5120/8204-1601
Abstract
Software effort estimation assesses the quantity of work required to develop a software project. It is a well known fact that the software industry is unable to give proper an estimate of effort, time and development cost and this is described in reports in various reports including those from project management consultancy companies through case studies on failed projects, and surveys. In this paper, we propose to investigate the Mean Magnitude Relative Error (MMRE) and Median Magnitude Relative Error (MdMRE) using various techniques such as M5, Linear regression, SMO Polykernel and RBF kernel. The dataset COCOMO is used for the investigations.
References
- M. Jørgensen, "A Review of Studies on Expert Estimation of Software Development Effort," J. Systems and Software, vol. 70, nos. 1-2, pp. 37-60, 2004. Suresh Nageswaran. Test effort estimation using use case points. Technology, (June), 2001
- Suresh Nageswaran. Test effort estimation using use case points. Technology, (June), 2001.
- Boehm, B. W. ,Software Engineering Economics. Prentice-Hall: Englewood Cliffs, N. J. , 1981.
- Putnam, L. H. , "A General Empirical Solution to the Macro Software Sizing and Estimating Problem," IEEE Transactions on Software Engineering, vol. se-4, no. 4, pp. 345–361, July 1978.
- Albrecht, A. J. and J. R. Gaffney, 'Software function, source lines of code, and development effort prediction a software science validation', IEEE Trans. on Softi. Eng. , 9(6), pp639-648, 1983.
- Boetticher G, Menzies T, Ostrand T (2007) PROMISE Repository of empirical software engineering data http://promisedata. org/ repository, West Virginia University, Department of Computer Science.
- L. C. Briand, V. R. Basili, and W. M. Thomas, "A Pattern Recognition Approach for Software Engineering Data Analysis," IEEE Trans. Software Eng. , vol. 18, no. 11, pp. 931-942, 1992.
- K. Molokken and M. Joorgensen, "A Review of Software Surveys on Software Effort Estimation," Proc. Intl Symp. Empirical Software Eng. , pp. 223-230, 2003.
- M. J. Shepperd, C. Schofield, and B. A. Kitchenham, "Effort Estimation Using Analogy," Proc. 18th Int'l Conf. Software Eng. , Berlin: IEEE CS Press, 1996.
- T. Mukhopadhyay, S. S. Vicinanza, and M. J. Prietula, "Examining the Feasibility of a Case-Based Reasoning Model for Software Effort Estimation," MIS Quarterly, vol. 16, pp. 155-171, June, 1992.
- Azzeh, M. , Neagu, D. , Cowling, P. , 2009. Fuzzy grey relational analysis for software effort estimation, Journal of Empirical software engineering.
- Mendes E, Mosley N, Counsell S (2003) A replicated assessment of the use of adaptation rules to improve Web cost estimation, International Symposium on Empirical Software Engineering, pp. 100-109.
- V. Vapnik, S. Golowich, and A. Smola. Support vector method for function approximation, regression estimation, and signal processing. In M. Mozer, M. Jordan, and T. Petsche, editors, Advances in Neural Information Processing Systems 9, pages 281–287, Cambridge, MA, 1997. MIT Press.
- C. -C. Chang and C. -J. Lin, LIBSVM: a library for support vector machines, 2001.
- Platt, J. C. ," Fast training of support vector machines using sequential minimal optimization". Advances in kernel methods: Support vector machines, B. Schokopf et al. (ed. ), MIT Press, 1999.
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