Traditionally software testing appears to be based upon risk and many models and examples of this have been published, just search the internet for ‘risk based testing’.
The following are a few examples from a quick search
The objective of Risk Analysis is to identify potential problems that could affect the cost or outcome of the project. Ståle Amland, 1999 http://www.amland.no/WordDocuments/EuroSTAR99Paper.doc
In simple terms – Risk is the probability of occurrence of an undesirable outcome ISTQB Exam Certification – What is Risk Based Testing 2014 - http://istqbexamcertification.com/what-is-risk-based-testing/
Risk:= You don’t know what will happen but you do know the probabilities, Uncertainty = You don’t even know the probabilities. Hans Schaefer, Software Test Consulting, Norway 2004 http://www.cs.tut.fi/tapahtumat/testaus04/schaefer.pdf
Any uncertainty or possibility of loss may result in non conformance of any of these key factors. Alam and Khan , 2013 Rsik Based Testing Techniques A perspective study http://www.academia.edu/3412788/Risk-based_Testing_Techniques_A_Perspective_Study
James Bach goes a little deeper and introduces risk heuristics
“Risk is a problem that might happen” James Bach 2003 Heuristics of Risk Based Testing http://www.satisfice.com/articles/hrbt.pdf
And continues with the following statement in the 'Making it All Work' section:
..don’t let risk-based testing be the only kind of testing you do. Spend at least a quarter of your effort on approaches that are not risk focuses..”
All of the examples above look at software testing and how to focus testing effort based upon risk they make no mention uncertainty. I have struggled to find any software testing models or articles on uncertainty which I feel can have value to the business in software projects. There are a few misconceptions of risk and uncertainty with people commonly mixing the two together and stating they are the same.
Some of the articles appear to follow the fallacy of mixing risk with uncertainty and attempting to measure uncertainty in the same way as risk. The issue I find with these articles in how you can measure something which has no statistical distribution?
One type of uncertainty that people attempt to measure is the number of defects in a product. Using complex formulas based upon lines of code or some other wonderful statistical model. Since the number of defects in any one product is uncertain I am unsure of the merits of such measures and their reliability.
The concern here is how would you define a defect? Surely it is not only based upon the number of lines of code or number of test cases defined, but upon the uniqueness of each and every user? In other words what some may see as defects others will gladly ignore and say it is ok, it is the character of the program.
Let’s look at what we mean by risk and uncertainty:
- Risk: We don’t know what is going to happen next, but we do know what the distribution looks like.
- Uncertainty: We don’t know what is going to happen next, and we do not know what the possible distribution looks like.
What does this mean to the lay person?
Risk can be judged against statistical probability for example the roll of a dice. We do not know what the outcome (roll) will be (if the dice is fair) but we know the outcome will be a number between 1 and 6 (1 in six chance).
Uncertainty is where outcome is not known and there is no statistical probability. An example of uncertainty is what does your best friend intend to eat next week on Thursday at 5pm. Can you create a probability model for that event?
Basically risk is measurable uncertainty is not.
“To preserve the distinction which has been drawn in the last chapter between the measurable uncertainty and an unmeasurable one we may use the term "risk" to designate the former and the term "uncertainty" for the latter.” : - Risk, Uncertainty, and Profit Frank Knight 1921 - http://www.econlib.org/library/Knight/knRUP7.html
The problem is that many people see everything as a risk and ignore uncertainty. This is not a deliberate action and is how our brains work to deal with uncertainty. The following psychological experiment shows this effect
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Let’s play a different thought experiment. Imagine there are two urns.
- Urn A contains 50 red marbles and 50 white marbles.
- Urn B contains an unknown mixture of red and white marbles (in an unspecified ratio).
You can select either of the Urns, and then select from it a random (unseen) marble. If you pick a red marble, you win a prize. Which Urn do you pick from?
In theory, it should not matter which urn you select from. Urn A gives a 50:50 chance of selecting a red marble. Urn B also gives you the same 50:50 chance.
Even though we don’t know the distribution of marbles in the second urn, since it only contains red and white marbles, this ambiguity equates to the same 50:50 chance.
For various reasons, most people prefer to pick from Urn A. It seems that people prefer a known risk rather than ambiguity.
People prefer to know the risk when making a decision rather than base it on uncertainty.
Next experiment: This time there is only one urn. In this urn is a mixture or Red, White and Blue marbles.
There are 90 marbles in total. 30 are Red, and the other 60 are a mixture of White and Blue (in an unknown ratio). You are given a choice of two gambles:
- Gamble 1 you win $100 if you pick a Red marble.
- Gamble 2 you win $100 if you pick a White marble.
Which gamble do you take? Now that you've read a section above you will see that most people seem to select Gamble 1. They prefer their risk to be unambiguous. A quick check of the expected value of both gambles shows they are equivalent (with a ⅓ probability). They go with the known quantity.
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The summary of this is that we tend to trend towards known risks rather than uncertainty.
What has all of this to do with software testing?
The majority of our testing is spent on testing based upon risk, with outcomes that are statistically known. This is an important task to do however does it have more value than testing against uncertainty? Using automated tools it is possible to test against all the possible outcomes when we are using a risk based testing approach. Risk is based upon known probabilities which machines are good at calculating and working through.
Since it is difficult to predict the future of uncertain events and we find it even more difficult to adjust our minds to looking for uncertainties then an exploratory testing approach may provide good value against uncertainties. Tools here can be of use such as random data generators, emulators where the data used for testing is not based upon risk but is entirely random and can provide unexpected results.
The key message of this article is that we need to be aware of confusing uncertainty with risk and ask ourselves are we testing based upon risk today or upon uncertainty. Each has value however sometimes one has more value than the other.
