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As a professional tester, we encounter a variety of situations that require us to
come up with a value/number. In these cases, we do find it difficult to deal with
uncertainty, of those aspects whose “countability” is fuzzy. So the typical answers
seem to be based on seat-of-the-pants approach. In the few cases where we’ve
encountered a similar situation and solved it well, the estimates turn out to be
correct, otherwise it’s simply a shot in the dark, praying to God that it turns out to
Just because we don’t see a direct connect to this value does not mean that we
resort to “guesswork”. What is needed is scientific approximation, the keyword is
scientific. It is important to understand that an exact value may not be required,
rather it is the reasoning that allows us to come up with a value, and constantly
improving the reasoning to better the value.
Scientific approximations are vital to good testing to estimate effort, data sizes,
#users and so on. This talk is about understanding how we can do approximations
scientifically. Try it on your head!