Software effort estimation using machine learning techniques

Abstract

Effort Estimation is a very important activity for planning and scheduling of software project life cycle in order to deliver the product on time and within budget. Machine learning techniques are proving very useful to accurately predict software effort values. This paper presents a review of various machine-learning techniques using in estimation of software project effort namely Artificial Neural Network, Fuzzy logic, Analogy estimation etc. Machine learning techniques consistently predicting accurate results because of its learning natures form previously completed projects. This paper summarizes that each technique has its own features and behave differently according to environment so no technique can be preferred over each other.