Software Development Effort Estimation Using Neural Network Model

Area of software measurement in software engineering is active more than thirty years. There is a huge collection of researches but still no a concrete software cost estimation model. If we want to measure the cost-effort of a software project, we need to estimate the size of the software. A number of software metrics are identified in the literature ; the most frequently cited measures are LOC(line of code) and FPA(function point analysis). The FPA approach has features that overcome the major problems with using LOC as a measure of system size. This paper presents an neural networks(NN) models that related software development effort to software size measured in FPs and function element types. The research describes appropriate NN modeling in the context of a case study for 24 software development projects. Also, this paper compared the NN model with a regression analysis model and found the NN model has better estimative accuracy.