An Exploration of PNN and GRNN Models For Efficient Software Development Effort Estimation
B V Ajay Prakash, D V Ashoka, V N Manjunath Aradhya
Journal of Information Technology Review, Vol. 6, No. 2, 2015.
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Abstract:
Increasing demand for software made IT industries to develop high quality software within predetermined time
and budget. In order to accomplish these challenges, the software development process needs to effectively managed and
planned. In software development process, effort estimation is very important activity to manage and plan for effective
development of software projects. If estimation of software development effort is not accurately measured then entire software
project may lead to failure and dissipate the IT industry budget. Machine learning and data mining techniques have been
explored as an alternative to existing model COCOMO. This paper aims to explore artificial neural network models such as
probabilistic neural networks (PNN) and generalized regression neural networks (GRNN) model on various datasets to
accurately estimate the software development effort. The results are evaluated using Mean Magnitude of Relative Error
(MMRE), Magnitude of Relative Error (MRE).