Graph Based Transfer Learning with Orthogonal Tunning for Functionality Size Insights

Function Point Analysis (FPA) is a method in software engineering that focuses on identifying the functions provided by a software system to users, such as data input, processing, output, and database management. These functions are classified according to complexity to quantify the system’s size in functional point units. In this paper, we propose two graph neural networks: a Graph-based Similarity Detection Neural Network (GSDNN) and a Prior-Structural Information Graph Neural Network (PSI-GNN) with a pre-trained layer using transfer learning, to define the best model for functional size prediction and uncover patterns and trends in data. Additionally, the NESMA (Netherlands Software Metrics Users Association) method, from the functional families approach, will be in focus, where the ISBSG (International Software Benchmarking Standards Group) dataset, which provides standardized and relevant data for comparing software performance, was used to analyze 1704 industrial software projects. The goal was to identify the graph architecture with the smallest number of experiments to be performed and the lowest Mean Magnitude Relative Error (MMRE) using orthogonal-array tuning optimization via Latin Square extraction. In the proposed approach, the number of experiments is fewer than 8 for each dataset, and a minimum MMRE value of 0.97% was obtained using PSI-GNN. Additionally, the impact of five input features on the change in MMRE value was analyzed with the top-performing model, employing the SHAP (SHapley Additive exPlanations) feature importance method, visualized through GraphExplainer. The frequency of user-initiated transactions, quantified technically, emerged as the most significant determinant within the NESMA framework.