Myth Buster: Do Engineers Trust Parametric Models Over Their Own Intuition?
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CP_070612_Valerdi_Myth_ISPA.pdf
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228.96 KB
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5fe723df45a5e6c2ccec451ec46f4717
Author(s)
Valerdi, Ricardo
Date Issued
June 12, 2007
Abstract
This paper explores the abilities of engineers to estimate everyday tasks and their reliance on
their own intuition when performing cost estimates. The approach to answering these questions
is similar to that of the popular television show MythBusters which aims to separate truth from
urban legend using controlled experiments. In MythBusters, methods for testing myths and
urban legends are usually planned and executed in a manner to produce the most visually
dramatic results possible, which generally involves explosions, fires, or vehicle crashes. While
the question of parametric models versus intuition is not as exciting, we provide an interesting
result that demonstrates the difference between what is real and what is fiction in the world of
cost estimation.
Two heuristics, representativeness and anchoring, are explored in two experiments involving
psychology students, engineering students, and engineering practitioners. The first experiment,
designed to determine if there is a difference in estimating ability in everyday quantities,
demonstrates that the three groups estimate with relatively equal accuracy. The results shed light
on the distribution of estimates and the process of subjective judgment. The second experiment,
designed to explore abilities for estimating the cost of software-intensive systems given
incomplete information, shows that predictions by engineering students and practitioners are
within 3-12% of each other. Results also show that engineers rely more on their intuition than on
parametric models to make decisions.
The value of this work is in helping better understand how software engineers make decisions
based on limited information. Implications for the development of software cost estimation
models are discussed in light of the findings from the two experiments.
Subjects
cost estimates
intuition
parametric models
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