Artificial Neural Network with Hyperbolic Tangent Activation Function to Improve the Accuracy of COCOMO II Model

In software engineering, Constructive Cost Model II (COCOMO II) is one of the most cited, famous and widely used model to estimate and predict some important features of the software project such as effort, cost, time and manpower estimations. Lately, researchers incorporate it with soft computing techniques to solve and reduce the ambiguity and uncertainty of its software attributes. In this paper, Artificial Neural Network (ANN) with Hyperbolic Tangent Activation Function is used to improve the accuracy of the COCOMO II model and the backpropagation learning algorithm used in the training process. In the experiment, COCOMO II SDR dataset is used for training and testing the model. The result shows that eight out of twelve projects have a closer effort value of actual effort. It shows that the proposed model produces better performance comparing to sigmodal function.