Chapter 9: Individual Expert Judgment

Overview

Applicability of Techniques in This Chapter
	Use of Structured Process	Use of Estimation Checklist	Estimating Task Effort in Ranges	Comparing Task Estimates to Actuals
What's estimated	Effort, Schedule, Features	Effort, Schedule, Features	Size, Effort, Schedule, Features	Size, Effort, Schedule, Features
Size of project	S M L	S M L	S M L	S M L
Development stage	Early-Late	Early-Late	Early-Late	Middle-Late
Iterative or sequential	Both	Both	Both	Both
Accuracy possible	High	High	High	N/A

Individual expert judgment is by far the most common estimation approach used in practice (Jørgensen 2002). Hihn and Habib-agahi found that 83% of estimators used "informal analogy" as their primary estimation technique (Hihn and Habib-agahi 1991). A New Zealand survey found that 86% of software organizations used "expert estimation" (Paynter 1996). Barbara Kitchenham and her colleagues found that 72% of project estimates were based on "expert opinion" (Kitchenham et al. 2002).

Expert-judgment estimates of individual tasks form the foundation for bottom-up estimation, but not all expert judgments are equal. Indeed, as Chapter 7, "Count, Compute, Judge," indicated, judgment is the most hazardous kind of estimation.

When discussing "expert judgment," we need first to ask "expert in what?" Being expert in the technology or development practices that will be employed does not make someone an expert in estimation. Magne Jørgensen reports that increased experience in the activity being estimated does not lead to increased accuracy in the estimates for the activity (Jørgensen 2002). Other studies have found that "experts" tend to use simple estimation strategies, even when their level of expertise in the subject being estimated is high (Josephs and Hahn 1995, Todd and Benbasat 2000).

This chapter describes how to ensure that, when you use expert judgment, the judgment is effective. The discussion in this chapter is closely related to the discussion in Chapter 10, "Decomposition and Recomposition," which explains how to combine the individual estimates accurately.