Early Size Estimation using Machine Learning

The process of size estimation of a project is one of the key activities that forms the indispensable part of any project development process. This broadly involves the evaluation of the number of codes and packages that can only be analyzed once the project is completed. The presented paper aims at the size estimation through predictive analysis using Machine Learning (ML) architecture to offer best possible size and effort estimation to complete a project. The article also evaluates the existing research in the field of size estimation discussing the research trends inspired with the ML techniques as size prediction models. Finally, it presents comprehensive survey of the possible prospective of ML approaches for size estimation along with the detailed information in terms of strengths, limitations, evaluation metrics implemented for evaluation along with the datasets involved in the studies.