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I'd be more concerned with the number of testing scenarios and your test coverage of that. Happy-day scenarios are relatively easy to cover, and most people who write tests tend to cover those with about a 90% coverage rate (in my experience). However, the place many people I've seen don't test is the rainy-day scenarios - testing your error-handling. Do you correctly fail when you should fail?

For example, I own Excel::Template which has 3600 LOC over 28 files. (This includes POD and comments.) I have about 75-90% of the standard usages tested for - I only have 61 tests. But, each one of my scenarios is tested with 4 tests and I have 15 scenarios I test for. The reason I can run just 15 scenarios is that I have looked at what the featureset is and have written tests to deal with the commonly-used features. And, most importantly, I have shown myself, through code inspection, that the features do not bleed over into one another. So, I can test each feature (for the most part) in isolation and be fairly confident things will work together.

Now, I need to add another roughly 30 scenarios - all my error cases. I currently have no way of proving that this thing will fail correctly. I also want to add further branch and conditional tests, as shown by Devel::Cover. D::C shows me that I have a 77.9% overall coverage. It also shows me where my testing coverage is worst, which shows me where additional tests will have the most impact.

. . . I'm wondering if I'm being over-cautious in testing everything, . . .

Testing can never prove the abscence of bugs - it can only prove that the pathways you have tests for will work as expected, under the conditions you're testing them. Now, if you can show that you are testing all the usages the code can reasonably expect to see when in production, you've got a minimal amount of tests. You can always test more.

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In reply to Re: Test/ Code Ratio by dragonchild
in thread Test/ Code Ratio by moot

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