Abstract: The process of software development is complex, and one of its most critical aspects is estimating the amount of work required for various projects. However, accurately defining the exact amount of work needed in the early stages of production can be challenging. Researchers have been working on creating different machine and deep learning models to address this issue. These models, including single-approach models and multi-model ensembles, utilize optimization strategies to provide precise predictions. We propose a hybrid particle swarm optimization (PSO) based artificial neural networks (ANNs) model for software effort estimation (SEE), which has shown to outperform existing models. This model was tested on various datasets such as Albrecht, China Desharnais, Kemerer Kitchenham Maxwell, and Cocomo81. The hybrid PSO-optimized ANNs model has exhibited exceptional accuracy, as evidenced by consistently high R-squared (R2) values across multiple datasets. Additionally, the model has displayed low root mean square error (RMSE) and mean absolute error (MAE) values, indicating precise predictions. These outcomes affirm the model's precision and effectiveness. The model's small MAE further confirms its accuracy in predicting the required work during software development. With these remarkable results, the hybrid PSO-optimized ANNs model will undoubtedly play a crucial role in software development processes, providing accurate and precise predictions of the required work.

Key words: software projects, effort estimation, machine learning, optimization techniques