Artificial neural networks (ANN) are used as data analysis tools and are known for their learning and generalization ability (Shawky, Salwa, & El-Hafiz, 2016). ANN is a mathematical model (algorithmic) inspired by biology (Mittas, Papatheocharous, Angelis, & Andreou, 2015). Neural networks provide relationships between complex data through a learning phase (Rijwani & Jain, 2016). Types of neural networks used are general regression networks, polynomial neural networks, and probabilistic neural networks (Prakash & Viswanathan, 2017). ANN uses processing features called neurons, each having a mathematical function with specific inputs, a computational procedure, and outputs (Rijwani & Jain, 2016). According to Kaushik, Tayal, Yadav, and Kaur (2016), ANN models used in software estimation are the radial basis function network (RBFN) and function link artificial neural network (FLANN). The RBFN model offers a straightforward design, good generalizability, strong tolerance to noise, and learning ability (Kaushik et al., 2016). The FLANN method is suited when data is nonlinear and is less complicated (Kaushik et al., 2016). Although ANN is considered an algorithmic process, the network itself is not an algorithm, but rather a framework of learning algorithms.
ANN's principal characteristic is the ability to approximate nonlinear functions and is thus similar to traditional statistical techniques such as logical regression, statistical regression, and discriminant analysis (Mittas et al., 2015). The ANN method utilizes machine learning and pattern recognition for estimation and can discover relationships between the dependent and independent variables (Kaur, 2017). Artificial neural networks have gained popularity for software estimation prediction due to their ability to capture complex data and to disregard noise in the input data (Pospieszny et al., 2018). ANN uses data from previous software projects to provide outputs by inference through learned data (Rijwani & Jain, 2016). The ANN design, inspired by the biological nervous system processes information using computational elements (nodes) operating through weighted inputs (layers) to provide accurate estimates (Bilgaiyan et al., 2017; Mittas et al., 2015). Additionally, the more considerable the amount of historical data, the more accurate the estimation; thus, the ANN is most effective in achieving accurate software development estimations when historical data is available (Naik & Nayak, 2017).