Performance Analysis of FCM Based
ANFIS and ELMAN Neural Network in Software Effort Estimation
Praynlin Edinson1 and Latha Muthuraj2
1Department
of Electronics and Communication Engineering, V V College of
Engineering, India
2Department
of Computer Science and Engineering, Government College of Engineering, India
Abstract: One of the major challenges confronted in the software industry is the
software cost estimation. It is very much related to, the decision making in an
organization to bid, plan and budget the system that is to be developed. The
basic parameter in the software cost estimation is the development effort. It
tend to be less accurate when computed manually. This is because, the
requirements are not specified accurately at the earlier stage of the project.
So several methods were developed to estimate the development effort such as
regression, iteration etc. In this paper a soft computing based approach is
introduced to estimate the development effort. The methodology involves an
Adaptive Neuro Fuzzy Inference System (ANFIS) using the Fuzzy C Means
clustering (FCM) and Subtractive Clustering (SC) technique to compute the
software effort. The methodology is compared with the effort estimated using an
Elman neural network. The performance characteristics of the ANFIS based FCM
and SC are verified using evaluation parameters.
Keywords: Software development, cost, effort
estimation, process planning, ANFIS.
Received January 4, 2014; accept July 9, 2014