It looks like there are a lot of opinions or assumptions about unit tests and code coverage, most of them confusing of biased in several ways. For example, I’ve heard or read things like “this is fine, it has X% coverage”, or “checking for coverage on pull requests doesn’t help”, or “dropping the coverage level is not an issue”, and many more of the like.
This article aims to shed some light on the issue of unit testing with and code coverage. Hopefully by the end of it, we’ll get an idea of which of the previous statements are right and which are wrong (spoiler alert: they’re all wrong, but for different reasons).
It’s not a metric
I pretty much agree with Martin Fowler here , about the fact that test coverage is not a metric that indicates how good we are doing in terms of testing, but just a reference in order to identify parts of the code that need to be tested.
This leads me to the idea that coverage is not a goal, or and ending point, but just the starting point.
What I mean by this is that imagine that you find in the coverage report, that some lines are not being exercised, for example there are two functions that lack testing. Good head start, now we can write tests for them. Now you go on and write a test for the first one. After that, you run the tests again, and the coverage increased: the first function is now covered, so those lines no longer appear as missing in the report. Now you might be thinking on moving on to writing tests for the second function. That’s the trap. Testing should not stop there. What about the rest of the tests scenarios for that first function? What about testing with more input, different combinations of parameters, side effects, and more? That’s why it’s not a goal: we shouldn’t stop testing once we’ve satisfied the missing lines on the report.
Another reason why reaching 100% coverage it’s not a valid goal, is because
sometimes is unachievable. There are parts of the code that respond to
defensive programming , and have some statements like
for unreachable conditions, which logically, if the code works correctly, will
never run (actually, the fact that it doesn’t fail by reaching those lines when tests are
run, works as a way of making the code to “self-testable”, so to speak).
Necessity and sufficiency
And even if we reach that unrealistic goal: what does it mean to have 100% code coverage on unit tests? Can we rely on that to say that the code is fully tested, and there are no bugs? Absolutely not.
Here I am not even talking about path coverage (sometimes referred to as
multiple condition coverage). To clarify, it’s known that covering all
branches does not mean the program will run just fine. Even with functional
(manual or automated) testing. Suppose that we’re completely sure all paths are
covered, and the testing team has checked everything, therefore we’re sure all
the logic is sound. It still doesn’t mean the program is correct. There are
runtime considerations to be taken into account: what if there is a race
condition? (something hard to reproduce). What if the server is under heavy
load? (with a high load average), what if
malloc() at some point
returns NULL? What if the disk is full? Or if there is latency? What about
security? You get the point, the list of possible failure scenarios, is infinite.
Putting those considerations aside, the crux question is: can unit tests prove the logic (again, not behaviour in runtime, just the logic), to be correct? No, because, even with all statements analysed, there is still the possibility that things are left out.
To put it in another way: if the coverage is lower than total, then assume there are things that will go south (the famous “code without tests is broken by design”). If everything is covered, then for interpreted languages (like Python), it means something like “it compiles“. It’s syntactically correct, which doesn’t mean it’s semantically correct. For compiled languages, here I see little gain, except for the mere fact that checks at a very basic level that the code will run.
A high coverage is not enough
There is another interesting idea about coverage, nicely illustrated in the paper “how to misuse tests coverage” , which is that code coverage can only tell about the code that is there. Therefore, it can’t tell anything about potential bugs that due to missing code. It can’t detect faults of omission.
On the other hand, if instead of being just guided by the test coverage, we actually think about the test scenarios that are relevant for a unit of code, we’ll start thinking on new possibilities, inputs, and combinations that will logically lead to these faults being discovered, and as a result of that, the corrective code will be included. This is the key point: not just to settle for a high coverage, but for having a battery of meaningful tests that cover relevant scenarios, instead of lines of code.
Cover scenarios, not lines of code.
The truth is that software is complex. Really complex. There are a lot of things that can go wrong. Therefore, tests are a fundamental tool to at least ensure a degree of quality. It is logical to think that for each line of code there should be many more of tests. This applies for all projects, in all programming languages. Now, if for each function we should have at least many more of them just testing it, you’ll quickly get the picture that the relation between productive code and testing code should be in the ratio 1:N. Now, having 100% coverage (to say the best), can only mean an 1:1 ratio. It could be the case of a single test, covering the function, but not will sufficient cases.
Relation between tests and main code
Let’s take a look at SQLite, which is a project that seems to have a reasonable level of testing . According to the document that explains it’s testing strategy, we can see that it has many more lines of tests code than main code in the library.
To quote the document itself: the library contains roughly 122 KLOC , whereas the tests are about 91,596.1 KLOC (~90M LOC). The ratio is an impressive 745x.
In my opinion, this relation does not only apply to C projects, it’s something general to all programming languages. It’s just the reality of software. This is what it takes to build reliable software.
Now, with this idea in mind, knowing that we must have many more lines of testing code than productive code, because each possible function can have multiple outcomes, and has to be exercised under multiple scenarios (validation of input, combination of its internal conditions, and more), it becomes clear, that coverage does not mean that the code is thoughtfully tested at all. It then becomes evident that coverage is not the end, but the beginning of testing: once we’ve identified the lines that need to be checked, the tests won’t stop once they’ve been covered, they should stop once all possible scenarios have been properly verified. It also becomes evident that is expected to have many more times testing lines than main ones.
Don’t rely on coverage. Rely on thoughful testing.
This idea was presented in a lightning talk at EuroPython 2017, on Monday 10 of July. Here are the slides.
|||“How to misuse test coverage” - Brian Marick http://www.exampler.com/testing-com/writings/coverage.pdf This is an excellent paper, that discusses some important points about test coverage.|
|||1 KLOC means 1000 lines of code|