Re: Statistics to test data pairs
From: Richard Ulrich (Rich.Ulrich_at_comcast.net)
Date: 02/21/05
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Date: Mon, 21 Feb 2005 15:45:46 -0500
On Sat, 19 Feb 2005 00:16:16 +1100, "CW" <CW@nospam.com> wrote:
> I haven't used statistics for many years, so please bear with me if my
> questions sound rudimentary.
>
> Here is the scenario:
>
> I am responsible for testing a new billing application. I am thinking of
> testing using the following methodology.
>
> For each billing type, I would generate billing result from this new
> application (and as well as from the old billing application the new system
> is replacing).
>
> As the old and the new system are supposed to have identical inputs, the
> results are supposed to be the same. Of course, in real life, it will not be
> the case. The differences could be due to (1) error in the old billing
> application; (2) error in the new billing application and (3) error in both
> systems.
This is hard to imagine. You expect the computer-part of a
billing system to generate so many errors that you need to
compare them statistically?
- I think I've seen such a lousy system, but not where it
mattered.
>
> For each billing type, there are hundreds to thousands of result (grouped by
> customer and period).
>
> Say, I could generate 1000 pair of data (i.e., for 1000 customers) from
> both billing systems.
There are so many errors, that you expect dozens to show
up within 1000 bills-generated? AND you want to count them?
This is not how computer systems are validated, I think,
though my personal experience is limited.
You should have a file of previous errors that were fixed -
the new system should not re-create old problems.
You should have a computerized system of comparing
New vs. Old so that there is not an artificial limit of (say)
1000 pairs of data, simply because you can't review that many.
>
> Out of them, x pairs would be identical between the old and the new system,
> and 1000 - x would be different.
>
> I am thinking of picking a sub-sample, say N, out 1000 - x pairs for my
> testing purpose.
>
> What sort of statistical methodology I can employ to say, after having
> compared N pairs and finding the new billing system to be correct each and
> every time, I am 99% confident that the new billing system has generated
> correct data.
The "Mythical Man-Month" is a good book on software
engineering, or so I've been told.
-- Rich Ulrich, wpilib@Pitt.edu http://www.pitt.edu/~wpilib/index.html
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