Software Effort Estimation. Planning to Meet Schedule. Lewis Sykalski 5/01/2010. How Managers View Software Effort Estimation. Nebulous Vague No correlation to reality Why bother. Effects of Bad/No Estimation. Underestimation can lead to Schedule/Cost Over-runs:
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Planning to Meet Schedule
Most managers today are unaware of established software effort estimation methodologies or don’t account for unforeseen consequences when using a method.
This paper attempts to reconcile this by surveying several effort estimation approaches and gauging both the utility and inherent pitfalls in each.
Additionally, this paper will present a refined method for software effort estimation based on expert judgment and describe benefits of using said method.
where a & b come from historical data/curve-fitting
Effort = LOC*Productivity
Combines inherent simplicity of expert judgment method w/feedback control provided for in other models
Expert A: 40 hours
Expert B: 20 hours
Expert C: 5 hours
Expert D: 20 hours
Expert E: 30 hours
No historical data:
40*0.2+20*0.2+5*0.2+20*0.2+30*0.2 =23.0 hours
Everybody counts methodology…
If we had rules where we threw out experts’ estimates if they were wildly off: > 12.0 STDEV
(Where σi <12.0)
(Could be closer?)
You could alternatively tighten the standard deviation constraint to trust only the leading expert…
(Where σi = best)
You could also adjust for deviations in estimate (how far they are normally off and in what direction)