Analysis and Comparison of Neural Network Models for Software Development Effort Estimation

Kamlesh Dutta (Department of CSE, National Institute of Technology, Hamirpur, India), Varun Gupta (Amity School of Engineering and Technology, Amity University, Noida, India) and Vachik S. Dave (Department of CSE, National Institute of Technology, Hamirpur, India)
Copyright: © 2019 |Pages: 112
EISBN13: 9781522597063|DOI: 10.4018/JCIT.2019040106
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Prediction of software development is the key task for the effective management of any software industry. The accuracy and reliability of the prediction mechanisms used for the estimation of software development effort is also important. A series of experiments are conducted to gradually progress towards the improved accurate estimation of the software development effort. However, while conducting these experiments, it was found that the size of the training set was not sufficient to train a large and complex artificial neural network (ANN). To overcome the problem of the size of the available training data set, a novel multilayered architecture based on a neural network model is proposed. The accuracy of the proposed multi-layered model is assessed using different criteria, which proves the pre-eminence of the proposed model.
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