Enhanced Software Effort Estimation using Multi Layered Feed Forward Artificial Neural Network Technique

Software Effort Estimation models are hot topic of study over 3 decades. Several models have been developed in these decades. Providing accurate estimations of software is still very challenging. The major reason for such disappointments in projects are because of inaccurate software development norms; effort estimation is one such practice. Dynamically fluctuating environment of technology in software development industry make effort estimation further perplexing. One of the most commonly used algorithmic model for estimating effort in industry is COCOMO. Capability of machine learning particularly Artificial Neural Networks is to adjust a complex set of bond among the various independent and dependent variables. The paper proposes usage of ANN (Artificial Neural Network) based model technologically advanced using Multi Layered Feed Forward Neural Network which is given training with Back Propagation training method. COCOMO data-set is accustomed to test and train the network. Mean-Square-Error (MSE) and Mean Magnitude of Relative-Error (MMRE) are used as performance measurement indices. The experiment outputs suggest that the suggested model can provide better results and accurately forecast the software development effort. (C) 2016 The Authors. Published by Elsevier B.V.