Abstract:
Many comparative studies on the performance of machine learning (ML) techniques
for web cost estimation (WCE) have been reported in the literature. However, not much
attention have been given to understanding the conceptual differences and similarities
that exist in the application of these ML techniques for WCE, which could provide
credible guide for upcoming practitioners and researchers in predicting the cost of new
web projects. This paper presents a comparative analysis of three prominent machine
learning techniques – Case-Based Reasoning (CBR), Support Vector Regression (SVR)
and Artificial Neural Network (ANN) – in terms of performance, applicability, and their
conceptual differences and similarities for WCE by using data obtained from a public
dataset (www.tukutuku.com). Results from experiments show that SVR and ANN provides
more accurate predictions of effort, although SVR require fewer parameters to generate
good predictions than ANN. CBR was not as accurate, but its good explanation attribute
gives it a higher descriptive value. The study also outlined specific characteristics of the
3 ML techniques that could foster or inhibit their adoption for WCE.