Effort Estimation of Web-based Applications using Machine Learning Techniques

Effort estimation techniques play a crucial role in planning of the development of web-based applications. Web based software projects, considered in the present-day scenario are different from conventional object oriented projects, and hence the task of effort estimation is a complex one. It is observed that the literature do not provide a guidance to the analysts to use a particular model as being the most suitable one, for effort estimation of web-based applications. A number of models like IFPUG Function Point Model, NESMA, MARK-II, etc. are being considered for web effort estimation purpose. The efficiency of these models can be improved by employing certain intelligent techniques on them. Keeping in mind the end goal to enhance the efficiency of evaluating the effort required to develop web based application, certain machine learning techniques such as Stochastic Gradient Boosting and Support Vector Regression Kernels are considered in this study for effort estimation of web based applications using IFPUG Function Point approach. The ISBSG dataset, Release 12 has been considered in this study for obtaining the IFPUG Function Point data. The performance effort estimation models based on various machine learning techniques is assessed with the help of certain metrics, in order to examine them critically.