New Computational Intelligence Paradigm for Estimating the Software Project Effort

Recently, there are numerous techniques have been proposed to forecast and identify the software development effort; such prediction has a prominent impact on the success of software development projects. The most common methods for estimating software development efforts that have been proposed in literature are: line of code (LOC)-based constructive cost model (COCOMO), function point- based regression model (FP), neural network model (NN), and case-based reasoning (CBR). Recent research has tended to focus on the use of function points (FPs) in estimating the software development efforts, however, a precise estimation should not only consider the FPs, which represent the size of the software, but should also include various elements of the development environment for its estimation. Therefore, the main benefit of this study is to design and analyze both function points and development environments of recent software development cases. Therefore, this paper presents a new intelligence paradigm scheme based on functional network to forecast that emphasizes on numerous software development elements. Both implementation and learning process are based on the utilization of functional networks as a new modeling scheme and investigate its efficiency as a software development estimation model for predicting the software development efforts.