Software Effort Prediction is the process of estimating the effort required to develop software. Effectively controlling the expensive investment of software development is achieved by accurately estimating the effort. Effort estimation at the early stage of software development is very difficult because of lot of uncertainty in input parameters which decides the software effort. Adaptive Neuro fuzzy Inference system (ANFIS) model deals effectively with uncertainty and provides reliable effort estimates In this paper ANFIS is proposed for software effort estimation is discussed. Dataset used for analysis purpose is of COCOMO II format which is the 93, 63 Historic dataset of NASA. COCOMO II consists of 17 Effort multipliers, 5 Scale factors, 1 LOC. Attributes like RUSE, PCON, and SITE play a least significant role in predicting the effort in COCOMO II Model these attributes are discarded in this approach. The ANFIS is modeled for several type of membership functions like Gaussian curve, Difference of sigmoidal membership, Gaussian combination membership, Generalized bell shaped membership, Product of sigmoidal membership, Trapezoidal membership, Triangular membership functions. From the experimental results, it was concluded that the proposed ANFIS model using Trapezoidal membership function has low MMRE (Mean Magnitude of Relative Error) than the above mentioned membership functions.

1. M. Sheppard, G. Kadoda, Comparing software prediction techniques using simulation, IEEE Trans. Software Eng. 27 (11) 1014–1022, November 1999.
2. S. Chulani, Bayesian Analysis of Software Cost and Quality Models, Ph. D. Dissertation, University of Southern California, Los Angeles, 1999.
3. Barry Boehm, COCOMO II: Model Definition Manuel. Version 2. 1, Center for Software Engineering, USC, 2000.
4. Xishi Huang, Danny Ho, Jing Ren, Luiz F. Capretz, "Improving the COCMO model using the Neuro fuzzy approach", Journal of applied soft computing, pp 29- 40, 2007.
5. J. S. R. Jang, Chuen-Tsai Sun, Eiji Mizutani, "Neuro – Fuzzy and Soft Computing", Prentice-Hall, 2006
6. Anish Mittal, Kamal Prakash, Harish Mittal, " Software Cost estimation using fuzzy logic", ACM SIGSOFT software Engineering Notes, Vol. 35, Nov 2010.
7. Taeho Lee, Donoh Choi, Jongmoon Baik, "Empirical Study on Enhancing the Accuracy of Software Cost Estimation Model for Defense Software Development Project Applications", ISBN 978-89-5519-146-2 " ICACT, pp. 1117- 1122, Feb. 2010.
8. Donald J. Reifer, Barry W. Boehm and Sunitha chulani, "The Rosetta stone: Making COCOMO 81 Estimates work with COCOMO II", CROSSTALK The Journal of Defense Software Engineering, pp 11 – 15, Feb. 1999.

Software Cost Estimation Adaptive Neuro Fuzzy Inference System (anfis) Effort Constructive Cost Model (cocomo) Mean Magnitude Of Relative Error (mmre).