Performance analysis of FCM based ANFIS and ELMAN neural network in software effort estimation

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.

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