An Iterative Estimation Technique: Wideband Modified Delphi

An Iterative Estimation Technique: Wideband Modified Delphi

This method was introduced by Barry Boehm in the 1970s, [4] and it has spawned several variants. It is often referred to as the Wideband Modified Delphi . The principle is to bring several heads together on an issue and try to reach a consensus. When these "heads" used are the actual development team, this approach is more likely to get their commitment than would random numbers raining from above. This is roughly how it works:

[4] See Boehm 1981.

  1. The project manager defines what is to be estimated, the units of measure, and the assumptions. The manager then gathers data for similar tasks or projects if available (for example, data from previous iterations or a previous project). Participants are selected.

  2. All the participants are briefed on the procedure and the goals, and given any available data.

  3. The participants develop their own estimate (each one on his or her own), preferably not interacting with each other.

  4. The project manager gathers all the data, tabulates it in a spreadsheet, and compares it.

  5. All participants meet again. Where the numbers match, they have a likely estimate. If the numbers are widely scattered , it is interesting to discuss with participants what motivated higher and lower numbers. What was the reasoning behind their estimate? When they explain their assumptions, other team members may react in one way or another: Some important parameter was forgotten, some new risk has arisen, and so on.

  6. The participants are then given a chance to adjust their estimate based on the discussion.

  7. The new numbers become the working estimate.

As the phase or iteration unrolls, actual data is then collected for these tasks and is used in the next estimate. At the next round (the next iteration, for example), when it is time to do another estimation, the previous estimates and the actual numbers are given to the participants, to help them adjust their natural optimism or pessimism.

There are plenty of variants and refinements, as you can imagine. You could iterate on steps 5 and 6 (although it is often not necessary). You can choose an informal route, using e-mail or simply walking from cubicle to cubicle with a notepad to discuss planning hypotheses with the people who had given great variance. You can do it very formally , using templates and tools to compute ranges and uncertainties, even using Monte Carlo simulations to generate a probability distribution of possible estimate outcomes based on the final estimate values. See Wiegers 2000 for another, more detailed description of this Wideband Delphi estimation technique.



The Rational Unified Process Made Easy(c) A Practitioner's Guide to Rational Unified Process
Programming Microsoft Visual C++
ISBN: N/A
EAN: 2147483647
Year: 2005
Pages: 173

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