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Project Management Estimation Methods – Expert Judgement

Posted by mgocean on May 25, 2009 in Estimation, Project Management |

There are at least four reasons why the algorithmic models do not perform well. First, there is the problem of software sizing. Obtaining consistent size estimates (in lines of code or Function Points) and interpreting such estimates require high skill levels and good judgment. Second, there is the issue of historical data. Most researchers have used (and require) a large volume of site-specific historical data to build or calibrate (i.e., tune) their models. When such models are transported to other sites, used at other times, or applied to other sets of projects, these models lose their validity and the resultant estimates become inaccurate. Third is the problem of data analysis. The dominant method of data analysis has been linear regression. A regression analysis may expose a statistical relationship between development factors and effort, but it provides little understanding of why or under what conditions the relationship exists. Furthermore, the assumptions underlying this approach (e.g., factor independence) are often questionable. Finally, there is the problem of task complexity. Many factors can affect the effort required. Different models capture different sets of factors. For example, in an analysis of five models, Wrigley and Dexter (1987) identified 74 factors indicating “the diversity and ambiguity surrounding these models.”Despite the large number of models developed in recent years, the software-estimation task remains a difficult problem.

Expert judgment techniques tend to be informal and qualitative in nature, even though they often produce quantitative results. The main source of information is the estimator’s memory of past projects, products, or processes. Although the experts may consult historical records to refresh their memories, these techniques do not use historical data in an organized way, even though they may perform quantitative calculations.

The difficulty with expert judgment methods is that the results are no better than the participants involved. Every individual has incomplete recall and may even have biases. For example, a developer may recall that she took six calendar-months to write the code for a particular subsystem, but may not recall that she worked an average of 50 hours per week during this six-month period. (For example, the hours worked per week are usually high just before the “gold master” disk is cut for a shrink-wrapped product, raising the average.) If so, this represents an underestimate of 25% (= 10/40). Hopefully, the experts chosen to estimate particular quantities are experienced and have good memories of relevant products, projects, or processes.

 

Expert judgment techniques do have some advantages, however. Experts are often able to assess whether the new object is representative of similar objects developed in the past. They are also good at identifying exceptional circumstances, major differences, and interactions between the projects, tasks, and system components. Biases can arise if there are external influences exerted on the estimator. (Stutzke R. D., 2005)

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