Monday, 21 April 2014

On Test Results and Decisions about Test Results

A Test Result != Result of a Test
An Observation != Result of an Observation

It’s not the test result that matters, but the decision about the test result!!

Pass-Fail-Dooesn’t Start-Inconclusive-Can’t Execute

How you come to these results (and there are more) is interesting. But from a testing viewpoint what you do, what actions you take based on such results is very interesting.
“If a tree falls in a forest and no one hears it, does it make a noise?”
“If a test result is obtained and not used or considered, was it worth it?”
Did it confirm an expectation?
Did it contribute to a body of evidence that a feature, product or system is behaving within bounds?
Did it help understand risks with the system?

If the answer is no, then it might be time to take a good long hard look at what you’re doing…

Body of Evidence / Evidence
All test observations and test results are not equal. They contribute to the picture of a product in different ways. But that picture is not necessarily a paint-by-numbers book. It’s not something that you can necessarily think I’ve ticked all the boxes, I’m finished.
Note: In many cases, Testing is not painting by numbers! 
Unless you’re a doctor finding no pulse and rigor mortis has set in!

It’s about making sense of the observations.

The 1% Problem Soundbite
Suppose 1% of your tests fail. Suppose you’ve seen a problem that 1% of your customers will experience.

The 50% Problem Soundbite
Suppose 50% of your tests fail. Suppose you’ve seen a problem that 50% of your customers will experience.

Based on this information is it possible to say anything about the product?

No. But you might have something that says, “need more information”.

These are what I think of as context-free reports.

From those soundbites, you don’t know anything about the nature of the problems, product, market that the product might be used in, circumstances for usage, etc, etc.

Suppose the 1% problem is a corner case - not allowing installation in a geographical location if some other feature is active - that might affect how the product is launched in that market. Suppose it’s something “cosmetic” that might annoy 1%, but not prevent the product being used. These two different types of observations (results) might produce totally different results.

The 50% case - is this a break in legacy, some feature that customers are using and needs to be operated differently; or is it a new feature interacting differently with existing features (e.g. flagged by some automated scripts) that hasn’t been launched yet and might be only for trial/“friendly users”. Again these observations (of essentially first order feedback, after Weinberg, ref [3]) might have totally different results.

Decisions and Supporting Data

1. Are you a tester that decides when testing is finished and a product can ship?
2. Are you a tester that tries to give your boss, project manager, stakeholder a good account of the testing done on a product? Might you even give some insight and feedback about what that testing might mean?

Ok, assuming you’re in the second category…

What does your test observation tell you?
What do your test observations tell you?

I’m reminded of a model I’ve helped use in the past about understanding test results, ref [1]. But now I’m looking at the flip-side, not what a “pass” means but what a “not-pass” might mean. A simple version of a test result / observation might look like:

Note, this is a typical interpretation when a test result is deemed to be of the form OK / NOK (including inconclusive, don’t know etc.) The implication of this is that when the desired test result (OK, pass) is obtained then that “test case” is done, i.e. it is static and is unchanged by environment or circumstance. This might be typical in conformance testing, where responses within a desired range is expected, usually when the test subject has a safety component (more on this in another post).

But, if the results are not black-and-white or can be open to interpretation (as in the 1% problem soundbite) then a different model might be useful.

This model emphasises the project, product and testing elements to consider - these may change over time (within the same project), between product versions and between testing cycles (or sprints). I’ve drawn the line between product owner and product decision as a thick black line to highlight that this is the deciding input.
More on testing and silent evidence can be found in ref [2].

A larger version of this picture can be found here.

[3] Quality Software Management: Vol 2 (Weinberg; 1993, Dorset House)

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