Empirical Comparison of Neural Network Architectures for Prediction of Software Development Effort and Duration

Accurately estimating the effort and duration required for software development is one of the most important challenges in the field of software engineering. In a context where software projects are becoming increasingly complex, project managers face real difficulties in meeting established deadlines and staying within budget constraints. The purpose of this research study is to identify which type of artificial neural network is most suitable for estimating the effort and duration of software development, given the relatively small size of existing datasets. In the process of software effort and duration prediction, four datasets were used: China, Desharnais, Kemerer and Maxwell. Additionally, different types of artificial neural networks were used: Multilayer Perceptron, Fractal Neural Network, Deep Fully Connected Neural Network, Extreme Learning Machine, and Hybrid Neural Network. Another goal of this research is to analyze the impact of a new and innovative hybrid architecture, which combines Fractal Neural Network with Random Forests in the estimation process. Five metrics were used to compare the accuracy of artificial neural networks: mean absolute error, median absolute error, root mean square error, coefficient of determination, and mean squared logarithmic error. Python 3.11 programming language was used in combination with TensorFlow, Keras, and Scikit-learn libraries to implement artificial neural networks.