Software effort estimation based on inception network optimized by enhanced banyan tree growth optimizer

The ability to estimate the cost of software projects is critical to achieving project success, however, standard estimation methods fail on their promise due to the complicated relationships that exist within project data. This paper proposes a new hybrid approach that combines an Inception Network with Enhanced Banyan Tree Growth Optimizer (EBTGO) for improved accuracy. The Inception Network can capture hierarchical features better from the high-dimensional data set, while EBTGO optimizes hyperparameters to improve model performance. Our two benchmark dataset experiments, the Maxwell (1995) and COCOMO81 (Boehm, 1981) data sets, confirm that the hybrid model outperforms the state-of-the-art in each round of testing and across all evaluation metrics including Mean Absolute Error (MAE), Mean Magnitude of Relative Error (MMRE), and others. The statistical tests provide evidence that the added improvements are at least reasonably significant. The visual analyses, including receiver operating characteristic (ROC) curves and confusion matrices, reinforce that the model is robust. In addition, this paper demonstrates the limitations of traditional and machine learning approaches for estimation and shows the potential of deep learning and metaheuristic optimization for predicting software effort. Finally, this is an optimistic advancement toward predictive modeling in software engineering.