The key to unlock the tests is in the combination

In the last blog, Andrew Schiano discussed Equivalence Class (EQ) and Boundary Value Analysis (BVA) testing methodologies.  This blog will talk about how to extend those two ideas even further with Combinatorial Testing.

Combinatorial Testing is a form of model-based testing.  It chooses pairs or sets of inputs, out of all of the possible inputs, that will give you the best coverage with the least cost.  Fewer test cases while still finding bugs and giving high code coverage is a dream of us testers.  It is best applied when:

  • Parameters are directly interdependent
  • Parameters are semi-coupled
  • Parameter input is unordered

Let’s look at an example UI.  You have to test a character formatting dialog.  It allows you to pick between four fonts, two font styles, and three font effects.  A chart of the values looks like this:

Field Values
Font Arial, Calibri,Helvetica, BrushScript
Style Bold, Italic
Effects Strikethrough, Word Underline, Continuous Underline

For any selection of text, you can have only one Font, zero to two Styles, and zero to two effects.  Notice how for effects, you could select one pair, strikethrough and continuous underline, but you should not be able to pick another pair, word underline and continuous underline.  You could set up a matrix of tests and check each combination.  That’s old school.  You could reduce the number of cases by applying EQ.  That would be smarter. Your best bet, though, is to apply Combinatorial testing.

Combinatorial testing, also sometimes called Pairwise Testing, is just modeling the System Under Test (SUT).  You start taking unique interesting combinations of inputs, using them to test the system, and then feeding that information back into the model to help direct the next set of tests.  There is mathematics involved that take into account the coupling (interactions) of the inputs, which we won’t cover here.  Thankfully there are tools that help us choose which test cases to run.   I’ll cover the Pairwise Independent Combinatorial Testing (PICT) tool, available free from Microsoft.  Since it is a tool, it is only as good as the input you give it.

The steps to using PICT or any other combinatorial testing tool are:

  1. Analysis and feature decomposition
  2. Model parameter variables
  3. Input the parameters into the tool
  4. Run the tool
  5. Re-validate the output
  6. Modify the model
  7. Repeat steps 4-6

In our example above, the decomposition would look like this:

  • Font: Arial, Calibri, Helvetica, BrushScript
  • Bold: check, uncheck
  • Italic: check, uncheck
  • Strikethrough: check, uncheck
  • Underline: check, uncheck, word, continuous

You feed this data into the tool and it will output the number of tests you specify.  You need to validate the case before running them.  For example, the BrushScript font only allows Italic and Bold/Italic.  If the tool output the test case:

  • Font: BrushScript
  • Bold: check
  • Italic: uncheck
  • Strikethrough: check
  • Underline:  word

Being the awesome tester that you are, you would notice this is not valid.  Thankfully the PICT tool allows you to constrain invalid input combinations.  It also allows you to alias equivalent values.  So, you modify the model, not the outputted values.  In this case, you would add two line so the input file would now look something like this:

Font: Arial, Calibri, Helvetica, BrushScript

Bold: check, uncheck

Italic: check, uncheck

Strikethrough: check, uncheck

Underline: check, uncheck, word, continuous

IF [Font] = “BrushScript” AND [Italic] = “uncheck” THEN [Bold] <> “check”;

IF [Bold] = “uncheck” AND [Italic] = “uncheck” THEN NOT [Font] = “BrushScript”;

The PICT tool also allows you to weight values that are more common (who really uses BrushScript anymore?), and seed data about common historical failures.

Font: Arial(8) , Calibri(10), Helvetica(5), BrushScript (1)

Does this really work?  Here is an example from a previous Microsoft project:

Command line program with six optional arguments:

Total Number of   Blocks = 483
Default test suite
Exhaustive   coverage
Pairwise coverage
Number of test   cases
9
972
13
Blocks covered
358
370
370
Cove coverage
74%
77%
77%
Functions not   covered
15
15
15

Now, pair this with automation so you have data-driven automated testing, and you’re REALLY off and running as a twenty-first century tester!

A few words of caution.  While this gives you the minimum set of tests, you should also test:

  1. Default combinations
  2. Boundary conditions
  3. Values known to have caused bugs in the past
  4. Any mission-critical combinations.

Lastly, don’t forget Beizer’s Pesticide Paradox.  Keep producing new test cases.  If you only run the tool once, and continually run those same cases, you’re going to miss bugs.

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2 Responses

  1. I’ve used PICT before to do some interesting stuff to help speed up determining what combination of renderer, codec, and video encoding causes problems on different pieces of hardware.

  2. Thanks for your post. As long as you don’t mind command lines, PICT is a powerful tool.

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