So Let’s Test The POJOs

So the code coverage is mediocre and there are loads of getters and setters and equals methods out there sitting untested. Let’s impress our lords and masters by running some tests against them. Assert that when you set the value, the getter gets the new value and so on.

Shall we?

Is POJO coverage important?

Here are some thoughts.

Code Coverage is less important than Code-that’s-used

Writing placebo tests is just busy work. Asking why the methods aren’t already used by tests that matter for your business scenarios will reveal that either:

  • Not all scenarios are covered – so do that
  • Well, this method isn’t really needed – so delete it

And in a few cases, that’ll still leave some stuff behind that’s kind of needed structurally, but isn’t that important right now.

Absence of code coverage creates a fog

While working on Spectrum, we maintained a rule of 100% code coverage (until some new coverage tool came along and lost us 0.01% for some reason). The idea was that you have a rigid feedback loop if your change does anything to a 100% coverage statistic. We reckoned you could test every scenario, so there shouldn’t be a line of code out of place.

When you have lower code coverage, it’s hard to tell from the statistics whether this means that important or unimportant stuff is not getting tested. When it’s higher – 95%+ – you get the impression that every uncovered line represents a testing misfire and a potential bug.

So find an easy way to cover your minor lines. Use an equals/hashcode test helper to wash away doubts about those methods. EqualsVerifier is one such option.

Testing things in context leads to high coverage

We’re not really here to make code coverage. We’re here to make code that’s guaranteed to do what the business process is expected to do. This means you need high scenario coverage. So maybe your getter/setter combination is only used when you serialize the object, so maybe you need to serialize some, perhaps in the context of the flow which need serialization to work.

Once you focus on the usage in real life of your classes, you can use code coverage to feedback that the intended code paths are tested and that all your code is actually needed.

Caveat Tester!

Despite all of this, code coverage is only a surrogate statistic. Alone it doesn’t prove anything. If it’s low it proves SOMETHING, but if it’s high, it only provides a number in response to what should be a genuine heartfelt attempt to try out the behaviour of your software and show it working as intended.

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