Evaluation of Software Development Effort Estimation via Computational Intelligence
Mahesh Bahadur Singh, Asif Uddin Khan · 2025
In software development, software engineering is reducing failure in development process. Before software development life cycle use 80% development suffer from cost and time factors. In SDLC processes Analysis phase calculate effort estimation by the system analysist because before development if team know about cost of project then failure chance will reduce. Effort estimation play a critical role for calculating effort in term of person/hour and time required. Basically traditional approach like COCOMO, FCP SLIM etc are used to calculate effort estimation. But if we want to calculate in automated form then Machine Learning approach are required. In Machine Learning ANN, SVR, Bayesian Network etc used for Effort Estimation. Machine Learning framework used the Data sets for estimating Effort. So high quality Datasets are required. Accuracy of effort estimation is directly proportional to Datasets in machine learning technique. In this paper we review the Data sets used in Machine Learning for Software Effort Estimation. and find which dataset is provide better result.