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# Thread: about oats(Orthogonal Array Testing Strategy)

1. ## about oats(Orthogonal Array Testing Strategy)

hello friends,

i'm studying oats(http://www.seilevel.com/OATS.html),this article list several steps to use this technique,two of those that i can't understand are:

3. Find a suitable orthogonal array ...

4. Map the factors and values onto the array

The article said these steps are simple and
straightforward, but i have no the feeling.
I have no guideline to obey,especially 4.

Maybe the question is i don't know exactly what is a orthogonal array, maybe not.

thanks for any response!

2. ## Re: about oats(Orthogonal Array Testing Strategy)

Orthogonal Arrays have been discussed here before, so please do a search.

For additional info, also see my article in STQE "Orthogonally Speaking," also at http://www.stickyminds.com/s.asp?F=S5031_MAGAZINE_2

To answer your questions:

3. Find a suitable orthogonal array ...

This is related to the fact that each testing effort has a variety of numbers of test parameters. The size of orthogonal array required is dependent on this number of test parameters.

4. Map the factors and values onto the array

Once you have chosen the suitable array, you will have to map the actual values (testing parameters) to the array values.

HTH,
Elfriede

3. ## Re: about oats(Orthogonal Array Testing Strategy)

Originally posted by lemon:
The article said these steps are simple and
straightforward, but i have no the feeling.
<font size="2" face="Verdana, Arial, Helvetica">Just to add to Elfriede's thoughts, as I looked at it, the article you mentioned states quite well how to do this. You have certain variables in an application. Not at the code level here, but in terms of the fields you can input information to or the procedures that you can feed input to (usually via those fields). Some fields require working together. (For example, last name and first name might be required for an interaction.) But others are independent. (Last name and first name may be independent of billing address, for example.) So you want to consider (as the article states) how many independent variables will be tested for in terms of interaction. These are the "factors". So a "suitable array" is an array that has at least as many factors as is needed from your analysis of only those factors that are independent. Likewise, you need to determine the maximum number of values that each independent variable will be able to take. These are your "levels". (So a given field in an application might be able to be filled in with different data; each data set is independent but each is also different.) So, adding to this, a "suitable array" should also have at least as many levels for each of the factors. The idea is that the array should be able to correlate the levels and the factors.

The article at the Seilevel site that you reference contains a fairly good simple example under the section "A Simple Example".

When you have an orthogonal array what that basically means is that the columns in your hypothetical array (composed of factors and levels) are such that the combination of the levels occurs the same number of times when two or more columns of that array are formed. So it is obvious that if you have a case of three factors and two levels for those factors then you have an orthogonal array that requires only four test conditions. Remember that with orthogonal arrays, all factor levels must occur in the array and factor levels must occur in equal numbers.

As far as you mentioning you are not sure if you know what orthogonal arrays are, the article you mentions gives a defintion: "Orthogonal arrays are two dimensional arrays of numbers which possess the interesting quality that by choosing any two columns in the array you receive an even distribution of all the pair-wise combinations of values in the array."

4. ## Re: about oats(Orthogonal Array Testing Strategy)

thank you very much!

i've see it with your help and you're all so kind.

thanks again.

regards
lemon

5. ## Re: about oats(Orthogonal Array Testing Strategy)

Just a point to remember about orthogonal arrays. The purpose of using this approach was to reduce the number of tests to find the OPTIMUM setting for a system. E.g if u had an engineering plant and wanted to know what the best temperature, humidity, mix, blah etc would produce the best engineered product (lets call this the factor x u are looking for). Then rather than test every variable lets call theses variables (a, b, c) these are termed as 'factors' also. We can also reduce the number of levels for these factors (levels being the given temperatures for example).
I have used this method extensively in the Engineering industry using a popular approach called the Taguchi method which is all part of a larger field called the design of experiments. The purpose was always to find out what the optimum setting was for a system using the least amount of tests and not just for the reducing the amounts of tests you did but to find the best setting. In this case this methodology is better suited for performance tests where you are trying to find the best settings for given variables/parameters or factors.

E.g. to conclude to get X(best) = A (at right level), B (at right level) and C(at right level).

Thats what the experiment was designed to do.
Hope this helps

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