Abstract
Software Effort Estimation (SEE) is used in accurately predicting the effort in terms of (person–hours or person–months). Although there are many models, Software Effort Estimation (SEE) is one of the most difficult tasks for successful software development. Several SEE models have been proposed. However, software effort overestimation or underestimation can lead to failure or cancellation of a project. Hence, the main target of this research is to find a performance model for estimating the software effort through conduction empirical comparisons using various Machine Learning (ML) algorithms. Various ML techniques have been used with seven datasets used for Effort Estimation. These datasets are China, Albrecht, Maxwell, Desharnais, Kemerer, Cocomo81, Kitchenham, to determine the best performance for Software Development Effort Estimation. Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and R-Squared were the evaluation metrics considered. Results and experiments with various ML algorithms for software effort estimation have shown that the LASSO algorithm with China dataset produced the best performance compared to the other algorithms.
