The use of realistic effort estimates is fundamental to both software and Web project management as they help project managers allocate resources, control costs and schedule, and improve current practices, leading to projects that are finished on time and within budget. Different effort techniques have been used to obtain effort estimates for Web projects. Two—stepwise regression and case-based reasoning—have already been presented in Chapters V and VI respectively. In this chapter we detail a third technique used to obtain effort estimates for Web projects, known as classification and regression trees (CART), that is considered a machine-learning technique. We detail its use by means of a case study where a real effort prediction model based on data from completed industrial Web projects is constructed step by step.
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