Improving modified cocomo ii artificial neural network using hyperbolic tangent activation function - Abdulaziz Al-Shalif, Sarah Abdulkarem (2017)
Software cost estimation is a complex and critical issue in software industry but it is an inevitable activity in the software development process. It is one of important factors for projects failure due to the ambiguity and uncertainty of software attributes at the early stages of software development. The estimation of effort in COCOMO II depends on several software attributes namely software size (SS), scale factors (SFs) and effort multipliers (EMs). Several researchers integrate COCOMO II with Artificial Neural Network (ANN) to overcome the ambiguous and uncertain of these attributes. However, ANN contributes to slow convergence caused by sigmoid function. Thus, this research proposes Hyperbolic Tangent activation function (Tanh) to be used in the hidden layer of the ANN architecture to produce faster convergence. Back-propagation learning algorithm is applied to the multilayer neural network for training and testing. The proposed activation function has been trained and tested using two different architectures of NN which are basic COCOMO II-NN and modified COCOMO II-NN that uses COCOMO II NASA93 dataset. The result has been compared to different activation functions namely Uni-polar sigmoid, Bi-polar sigmoid, Gaussian and Softsign. The experiment results indicate that Tanh with modified COCOMO II-NN architecture achieved 23.2780 % Mean Magnitude Relative Error (MMRE) for 19 testing projects and 9.8948 % MMRE for 9 testing projects which is the lowest MMRE among other activation functions. In conclusion, Tanh with modified architecture of COCOMO II-NN provides much better estimation results than other methods and can lead to improvement of software estimates.