Improving Accuracy of Software Development Effort Estimation Using Use Case Points and Fuzzy Logic

Effort estimation in software development is essential for effective project planning and resource management. The Use Case Points (UCP) method is one of the most recognized estimation techniques; however, its accuracy is often constrained by the subjectivity involved in determining the Environmental Complexity Factor (ECF). This study introduces an enhanced estimation model that integrates Fuzzy Logic into the UCP framework to reduce subjectivity and improve precision. Six software project datasets were analyzed—one institutional project and five publicly available datasets—using Python-based simulations. The proposed Fuzzy-UCP model redefines ECF through fuzzy membership functions and rule-based inference, transforming linguistic assessments into quantitative outputs. Evaluation metrics, including Mean Magnitude of Relative Error (MMRE) and Estimation of Mean Magnitude of Error (EMMER), were employed to assess prediction accuracy. The results demonstrate that the Fuzzy-UCP model improves estimation accuracy by 4% to 12% compared to the standard UCP method, with lower standard deviation values. These findings confirm that incorporating fuzzy reasoning enhances reliability in handling uncertainty during effort estimation. Consequently, the Fuzzy-UCP approach provides a practical, adaptive, and computationally efficient alternative for software engineering practitioners seeking consistent and data-driven estimation results.