Effort Estimation Techniques for Legacy Systems

The objective of this chapter is to provide an introduction to statistical techniques and concepts that are frequently used when dealing with data for effort estimation. The concepts presented here are in no way exhaustive since statistics comprises a very large body of knowledge where entire books are devoted to specific topics. The parts that are the focus of this chapter are those that are necessary to use when building effort estimation models, and also when comparing different effort estimation techniques.

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What is the Best Technique?

Cost Estimation Techniques for Web Projects ◽

10.4018/978-1-59904-135-3.ch008 ◽

2008 ◽

pp. 240-274

Author(s):

Emilia Mendes

Keyword(s):

Relative Error ◽

Prediction Models ◽

Statistical Significance ◽

Estimation Accuracy ◽

Estimation Technique ◽

Effort Estimation ◽

Data Set ◽

Estimation Techniques ◽

The Mean ◽

Effort Prediction

Although numerous studies on Web effort estimation have been carried out to date, there is no consensus on what constitutes the best effort estimation technique to be used by Web companies. It seems that not only the effort estimation technique itself can influence the accuracy of predictions, but also the characteristics of the data set used (e.g., skewness, collinearity; Shepperd & Kadoda, 2001). Therefore, it is often necessary to compare different effort estimation techniques, looking for those that provide the best estimation accuracy for the data set being employed. With this in mind, the use of graphical aids such as boxplots is not always enough to assess the existence of significant differences between effort prediction models. The same applies to measures of prediction accuracy such as the mean magnitude of relative error (MMRE), median magnitude of relative error (MdMRE), and prediction at level l (Pred[25]). Other techniques, which correspond to the group of statistical significance tests, need to be employed to check if the different residuals obtained for each of the effort estimation techniques compared come from the same population. This chapter details how to use such techniques and how their results should be interpreted.

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Web Development Effort Estimation

Handbook of Research on Web Information Systems Quality ◽

10.4018/978-1-59904-847-5.ch002 ◽

2011 ◽

pp. 26-56 ◽

Cited By ~ 1

Author(s):

Emilia Mendes ◽

Silvia Abrahão

Keyword(s):

Multivariate Regression ◽

Project Managers ◽

Case Based Reasoning ◽

Development Effort ◽

Web Development ◽

Effort Estimation ◽

Estimation Techniques ◽

Classification And Regression ◽

Case Based

Effort models and effort estimates help project managers allocate resources, control costs and schedule, and improve current practices, leading to projects that are finished on time and within budget. In the context of Web development and maintenance, these issues are also crucial, and very challenging, given that Web projects have short schedules and a highly fluidic scope. Therefore, the objective of this chapter is to introduce the concepts related to Web effort estimation and effort estimation techniques. In addition, this chapter also details and compares, by means of a case study, three effort estimation techniques, chosen for this chapter because they have been to date the ones mostly used for Web effort estimation: Multivariate regression, Case-based reasoning, and Classification and Regression Trees. The case study uses data on industrial Web projects from Spanish Web companies.

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