© 2019 Abdel Karim Baareh. This open access article is distributed under a Creative Commons Attribution (CC-BY) 3.0 license. Journal of Computer Science Original Research Paper Optimizing Software Effort Estimation Models Using Back- Propagation Versus Radial Base Function Networks Abdel Karim Baareh Computer Science Department, Ajloun University College, Al-Balqa Applied University, Ajloun, Jordan Article history Received: 21-11-2018 Revised: 11-01-2019 Accepted: 09-03-2019 Email: baareh@bau.edu.jo 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. Keywords: Effort Estimation, NASA Software, Artificial Neural Network, Back-Propagation, Radial Base Function Introduction Building and estimating successful software is an important task that attracted many software developers (Boraso et al., 1996; Dolado, 2011). Bidding, budgeting and planning are very important factors that affect project success. Accurate defining of these factors will reflect on the project size, time, efforts, complexity and the different required tools to avoid the sudden and unexpected events that may happen during the project duration, that cause a project loss. Good software estimation gives exact feedback about the project progress that allows better resource utilization, allocation and use (Boehm, 1981). In Software Technology Conference held in 1998, Dr. Patricia Sanders, Director of Test Systems Engineering and Evaluation at OUSD, stated that 40% of the DoD’s software development costs are wasted and paid on reworking the software, that caused an annual loss of $18 billion on the year of 2000. Dr. Patricia added that only 16% of the developed software could finish in the accurate time and budget. 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 Constructive Cost Model (COCOMO) is considered as one of the most important, popular and famous models used to estimate the software effort which is developed by Boehm (1981; Boehm et al., 1995). Numerous techniques were used by different researchers for building an efficient estimation models structure to process the software cost estimation problem. Artificial neural network with different architecture was one of these models that proved its solidity and efficiency in this field (Shepper and Schoeld, 1997) moreover, the fuzzy logic used by (Kumar et al., 1994; Kaushik et al., 2012) and evolutionary algorithms such as genetic algorithm and genetic programming was also strongly used to deal with such types of problems. Artificial neural network algorithm with back- propagation algorithm versus the radial base function is used in this paper. The comparison between the two models is presented. This comparison will contribute in