2019

COMPLEX CONDITIONALS

The other day we dealt with code coverage and gnarly conditionals. I promised to offer a way to be able to test them properly.

THERE IS NONE.

Ha, what a bad joke. But the real answer might not be better, depending on your point of view. What you have to do is create a table.

(A || B) && C

This is our conditional.

| m | A | B | C | RESULT |
----------------------
| 0 | T | T | T |    T   |
| 1 | T | F | T |    T   | x
| 2 | F | F | T |    F   | x
| 3 | F | T | T |    T   | x
| 4 | T | T | F |    F   |
| 5 | T | F | F |    F   | x
| 6 | F | T | F |    F   |
| 7 | F | F | F |    F   |

# m is the test case # A, B, C are the atomic parts of the conditional # RESULT is the result of the evaluation of the conditional

For three terms in a conditional, you can have 8 different cases (2^3). You don’t need to test every case. You have to find those cases where switching one term (A, B or C) changes the RESULT. You take those cases and write tests for them. You can ignore the rest as they don’t bring you any new information. For our example these could be the test cases 1, 2, 3, 4. I marked them with an x,

The general rule of thumb is that you can solve this with n + 1 test cases where n is the number of terms.

This technique is called Modified Condition/Decision Coverage or short MC/DC. I like this name, it’s easy to remember 🤘.

It gets harder to do, when one term of the conditional is used more than once (coupled). Another thing to take note of is that depending on the decision statement in the code, it may not be possible to vary the value of the coupled term such that it alone causes the decision outcome to change. You can deal with this by only testing uncoupled atomic decisions. Or you analyse every case where coupling occurs one-by-one. Then you know which ones to use.

You’ll have to do this in the aerospace software industry or where you deal with safety-critical systems.

If you read this far: Congratulations. This is one of the more in-depth topics of testing software. You deserve a 👏 for learning about these things!

CODE COVERAGE CAN BE MISLEADING

During the last week, I had two discussions about code coverage. Code coverage is the metric of how many lines of code are covered by your automated test suite. Many test frameworks have built-in ways to measure this. Other times you have to install another tool manually. When you run your tests you then see how many lines are not covered by a test. That means that no test was run where this line of code was evaluated or executed or interpreted.

When you reach 100% code coverage, what then? Are you done? Could you guarantee that there are absolutely no bugs in your code?

If you are tempted to say Yes, or “maybe?" then let me tell you that you are wrong.

Consider this piece of code.

If you write a unit test for this method, the line eval... will be interpreted because of the if emergency at the end. The line is thus covered. But the code is not covered or tested.

Admittedly, this is a very trivial example that I made up. In reality, there are some more profound things to consider.

If you have complex conditionals you might need a logic table where you compare all possible combinations of the atomic parts of the conditional.

You cannot possibly evaluate this in your head and know whether you checked for every possible, sensible combination. Yet when you cover that line you are at 100% coverage and can go home, right?

So what do you do? Let’s look at this tomorrow.