International Journal of Intelligent Systems and Applications(IJISA)

ISSN: 2074-904X (Print), ISSN: 2074-9058 (Online)

Published By: MECS Press

IJISA Vol.5, No.3, Feb. 2013

A Type-2 Fuzzy Logic Based Framework for Function Points

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Anupama Kaushik, A.K. Soni, Rachna Soni

Index Terms

Project management;Software Effort Estimation;Type-2 Fuzzy Logic System;Function Point Analysis


Software effort estimation is very crucial in software project planning. Accurate software estimation is very critical for a project success. There are many software prediction models and all of them utilize software size as a key factor to estimate effort. Function Points size metric is a popular method for estimating and measuring the size of application software based on the functionality of the software from the user’s point of view. While there is a great advancement in software development, the weight values assigned to count standard FP remains the same. In this paper the concepts of calibrating the function point weights using Type-2 fuzzy logic framework is provided whose aim is to estimate a more accurate software size for various software applications and to improve the effort estimation of software projects. Evaluation experiments have shown the framework to be promising.

Cite This Paper

Anupama Kaushik, A.K. Soni, Rachna Soni,"A Type-2 Fuzzy Logic Based Framework for Function Points", International Journal of Intelligent Systems and Applications(IJISA), vol.5, no.3, pp.74-82, 2013.DOI: 10.5815/ijisa.2013.03.08


[1]B.W. Boehm. Software Engineering Economics. Prentice Hall, Englewood Cliffs, NJ, 1981.

[2]B. Boehm, B. Clark, E. Horowitz, R. Madachy, R. Shelby, C. Westland. Cost models for future software life cycle processes: COCOMO 2.0. Annals of Software Engineering, 1995.

[3]L.H. Putnam. A general empirical solution to the macro software sizing and estimation problem. IEEE Transactions on Software Engineering, vol.4, 1978, pp 345-361.

[4]Moataz A. Ahmed, Zeeshan Muzaffar. Handling imprecision and uncertainty in software development effort prediction: A type-2 fuzzy logic based framework. Information and Software Technology Journal. vol. 51, 2009, pp. 640-654.

[5]Function Point Counting Practices Manual, fourth edition, International Function Point Users Group, 2004.

[6]G. Antoniol, C. Lokan, G. Caldiera, R. Fiutem. A function point like measure for object oriented software. Empirical Software Engineering. vol. 4, 1999, pp. 263-287.

[7]Fei. Z, X. Liu. f-COCOMO-Fuzzy Constructive Cost Model in Software Engineering. Proceedings of IEEE International Conference on Fuzzy System. IEEE Press, New York, 1992, pp. 331-337.

[8]J. Ryder. Fuzzy Modeling of Software Effort Prediction. Proceedings of IEEE Information Technology Conference. Syracuse, NY, 1998.

[9]A.R. Venkatachalam. Software Cost Estimation using artificial neural networks. Proceedings of the International Joint Conference on Neural Networks, 1993, pp. 987-990.

[10]K.K. Shukla. Neuro-genetic Prediction of Software Development Effort. Journal of Information and Software Technology, Elsevier. vol. 42, 2000, pp. 701-713. 

[11]Alaa.F.Sheta. An Estimation of the COCOMO model parameters using the genetic algorithms for the NASA project parameters. Journal of Computer Science, vol. 2, 2006, pp.118 -123. 

[12]Osias de Souza Lima Junior, Pedro Porfirio Muniaz Parias, Arnaldo Dias Belchior. A fuzzy model for function point analysis to development and enhancement project assessement. CLEI Electronic Journal, vol. 5, 1999, pp. 1-14.

[13]Ho Leung, TSOI. To evaluate the function point analysis: A case study. International Journal of computer, Internet and management vol. 13, 2005, pp. 31-40.

[14]G.R. Finnie, G.E. Wittig, J.M. Desharnais. A comparison of software effort estimation techniques: using function points with neural networks, case-based reasoning and regression models. Journal of Systems Software, Elsevier. vol. 39, 1977, pp. 281-289.

[15]M.A. Al-Hajri, A.A.A Ghani, M.S. Sulaiman, M.H. Selamat. Modification of standard function point complexity weights system. Journal of Systems and Software, Elsevier,vol. 74, 2005, pp. 195-206.

[16]O.S. Lima, P.F.M. Farias, A.D. Belchior. Fuzzy modeling for function point analysis. Software Quality Journal, vol. 11, 2003, pp. 149-166.

[17]C. Yau, H. L. Tsoi. Modelling the probabilistic behavior of function point analysis. Journal of 

Information and Software Technology, Elsevier. vol. 40, 1998, pp. 59-68.

[18]A. Abran, P. Robillard. Function Points Analysis: An empirical study of its measurement processes. IEEE Transactions on Software Engineering, vol. 22, 1996, pp.895-910.

[19]T. Kralj, I. Rozman, M. Hericko, A. Zivkovic. Improved standard FPA method- resolving problems with upper boundaries in the rating complexity process. Journal of Systems and Software, Elsevier, vol. 77, 2005, pp. 81-90.

[20]Wei Xia, Luiz Fernando Capretz, Danny Ho, Faheem Ahmed. A new calibration for function point complexity weights. Journal of Information and Software Technology, Elsevier. vol. 50, 2008 pp.670-683.

[21]Mohd. Sadiq, Farhana Mariyam, Aleem Ali, Shadab Khan, Pradeep Tripathi. Prediction of Software Project Effort using Fuzzy Logic. Proceedings of IEEE International Conference on Fuzzy System, 2011, pp. 353-358.

[22]A. Albrecht. Measuring application development productivity. Proceedings of the Joint SHARE/GUIDE/IBM Application Development Symposium, 1979, pp. 83-92.

[23] L. A. Zadeh. Fuzzy Sets. Information and Control, vol. 8, 1965, pp. 338-353.

[24]M. Wasif Nisar, Yong-Ji Wang, Manzoor Elahi. Software Development Effort Estimation using Fuzzy Logic – A Survey. Fifth International Conference on Fuzzy Systems and Knowledge Discovery, 2008, pp 421-427. 

[25]L. Wang. Adaptive Fuzzy System and Control: Design and Stability Analysis. Prentice Hall, Inc., Englewood Cliffs, NJ 07632, 1994.

[26]E.H. Mamdani. Applications of fuzzy algorithms for simple dynamic plant. Proceedings of IEEE, vol. 121, 1974, pp. 1585-1588.

[27]L. A. Zadeh. The Concept of a Linguistic Variable and Its Application to Approximate Reasoning–1. Information Sciences, vol. 8, 1975, pp. 199-249.

[28]J.M. Mendel, Q. Liang. Pictorial comparison of Type-1 and Type-2 fuzzy logic systems. Proceedings of IASTED International Conference on Intelligent Systems and Control, Santa Barbara, CA, October 1999.

[29]J.M. Mendel. Uncertain Rule-Based Fuzzy Logic Systems, Prentice Hall, Upper Saddle River, NJ 07458, 2001.

[30]E.H. Mamdani. Application of fuzzy logic to approximate reasoning using linguistic synthesis. IEEE transactions on computers, vol. 26, 1977, pp. 1182-1191.