Myth Buster: Do Engineers Trust Parametric Models Over Their Own Intuition?

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 legen...

पूर्ण विवरण

ग्रंथसूची विवरण
मुख्य लेखक: Valerdi, Ricardo
स्वरूप: Presentation
प्रकाशित: 2014
विषय:
ऑनलाइन पहुंच:http://hdl.handle.net/1721.1/84456
विवरण
सारांश: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.