Abstract
Software development effort estimation becomes a very important and vital tool for many researchers in different fields. Software estimation used in controlling, organizing and achieving projects in the required time and cost to avoid the financial punishments due to the time delay and other different circumstances that may happen. Good project cost estimation will lead to project success and reduce the risk of project failure. In this paper, two neural network models are used, the Back-propagation algorithm versus the redial base algorithm. A comparison is done between the suggested models to find the best model that can reduce the project risks related to time and increase the profit by achieving the demands of the required project in time. The two models are implemented on a 60 of NASA public dataset, divided into 45 data samples for training and 15 data samples for testing. From the result obtained we can clearly say that the performance of the back-propagation neural network in training and testing cases is actually better than the radial base function, so the back-propagation algorithm can be recommended as a useful tool in the software effort and cost estimation.
Highlights
Building and estimating successful software is an important task that attracted many software developers (Boraso et al, 1996; Dolado, 2011)
Two neural network algorithms were used in this paper, back-propagation algorithm compared to radial bases function
The different error estimation statistical functions results for the training and testing cases of the radial base function neural network can be shown in Table 4 and 5
Summary
Building and estimating successful software is an important task that attracted many software developers (Boraso et al, 1996; Dolado, 2011). Effort estimation was mainly affected by the Developed Line of Code (DLOC), where the instructions of the program and statements were included This model worked on 63 software projects and its core function based on finding and determining the arithmetical relationship between three important variables; the time of software development, human efforts during the work months and effort of maintenance (Kemere, 1987). The Artificial Neural Networks (ANN) consists of a great amount of fully and strongly interconnected cells called neurons, all working to gather in a systematic manner to solve specific problems, which learn by example similar to the way the human biological systems do.
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