Estimating the effort, time, and cost needed to build a software project is an important task in software engineering. Estimating software prior to development can help to reduce risk and improve the project success rate. Researchers have developed numerous traditional and machine learning models to estimate software effort, but it has always been difficult to estimate effort precisely. This paper presents a predictive model based on artificial neural networks namely ANNs to predict the software effort. The NASA dataset is applied to construct the proposed model. The system was trained using 50 data points, and the remaining 10 were used for testing. It was concluded that the ANN approach could estimate the software effort with high accuracy. A comparative study with other published equations was also performed, and it was found that ANN had less error and produced better results than other existing methods.
