I have a coworker who writes unit tests for objects which fill their fields with random data. His reason is that it gives a wider range of testing, since it will test a lot of different values, whereas a normal test only uses a single static value.

I've given him a number of different reasons against this, the main ones being:

  • random values means the test isn't truly repeatable (which also means that if the test can randomly fail, it can do so on the build server and break the build)
  • if it's a random value and the test fails, we need to a) fix the object and b) force ourselves to test for that value every time, so we know it works, but since it's random we don't know what the value was

Another coworker added:

  • If I am testing an exception, random values will not ensure that the test ends up in the expected state
  • random data is used for flushing out a system and load testing, not for unit tests

Can anyone else add additional reasons I can give him to get him to stop doing this?

(Or alternately, is this an acceptable method of writing unit tests, and I and my other coworker are wrong?)


Depending on your object/app, random data would have a place in load testing. I think more important would be to use data that explicitly tests the boundary conditions of the data.


Can you generate some random data once (I mean exactly once, not once per test run), then use it in all tests thereafter?

I can definitely see the value in creating random data to test those cases that you haven't thought of, but you're right, having unit tests that can randomly pass or fail is a bad thing.

Outlaw Programmer
  • if it's a random value and the test fails, we need to a) fix the object and b) force ourselves to test for that value every time, so we know it works, but since it's random we don't know what the value was

If your test case does not accurately record what it is testing, perhaps you need to recode the test case. I always want to have logs that I can refer back to for test cases so that I know exactly what caused it to fail whether using static or random data.

+3  A: 

There is a half-way house here which has some use, which is to seed your PRNG with a constant. That allows you to generate 'random' data which is repeatable.

Personally I do think there are places where (constant) random data is useful in testing - after you think you've done all your carefully-thought-out corners, using stimuli from a PRNG can sometimes find other things.

Will Dean
I have seen this work well on a system that had lots of locking and threads. The 'random' seed was writen to a file on each run, then if a run failed, we could work out the path the code took and write a hand written unit test for that case we had missed.
Ian Ringrose
+2  A: 

In the book Beautiful Code, there is a chapter called "Beautiful Tests", where he goes through a testing strategy for the Binary Search algorithm. One paragraph is called "Random Acts of Testing", in which he creates random arrays to thoroughly test the algorithm. You can read some of this online at Google Books, page 95, but it's a great book worth having.

So basically this just shows that generating random data for testing is a viable option.

+10  A: 

There's a compromise. Your coworker is actually onto something, but I think he's doing it wrong. I'm not sure that totally random testing is very useful, but it's certainly not invalid.

A program (or unit) specification is a hypothesis that there exists some program that meets it. The program itself is then evidence of that hypothesis. What unit testing ought to be is an attempt to provide counter-evidence to refute that the program works according to the spec.

Now, you can write the unit tests by hand, but it really is a mechanical task. It can be automated. All you have to do is write the spec, and a machine can generate lots and lots of unit tests that try to break your code.

I don't know what language you're using, but see here:





These tools will take your well-formed spec (i.e. type information) as input and automatically generate as many unit tests as you want, with automatically generated data. They use "shrinking" strategies (which you can tweak) to find the simplest possible test case to break your code and to make sure it covers the edge cases well.

Happy testing!

+1 to this. ScalaCheck does a phenomenal job of generating minimized, random test data in a repeatable way.
Daniel Spiewak
It's not random. It's arbitrary. Big difference :)

How can your guy run the test again when it has failed to see if he has fixed it? I.e. he loses repeatability of tests.

While I think there is probably some value in flinging a load of random data at tests, as mentioned in other replies it falls more under the heading of load testing than anything else. It is pretty much a "testing-by-hope" practice. I think that, in reality, your guy is simply not thinkng about what he is trying to test, and making up for that lack of thought by hoping randomness will eventually trap some mysterious error.

So the argument I would use with him is that he is being lazy. Or, to put it another way, if he doesn't take the time to understand what he is trying to test it probably shows he doesn't really understand the code he is writing.

Greg Whitfield
It's possible to log the random data or random seed so that the test can be reproduced.

We just ran into this today. I wanted pseudo-random (so it would look like compressed audio data in terms of size). I TODO'd that I also wanted deterministic. rand() was different on OSX than on Linux. And unless I re-seeded, it could change at any time. So we changed it to be deterministic but still psuedo-random: the test is repeatable, as much as using canned data (but more conveniently written).

This was NOT testing by some random brute force through code paths. That's the difference: still deterministic, still repeatable, still using data that looks like real input to run a set of interesting checks on edge cases in complex logic. Still unit tests.

Does that still qualify is random? Let's talk over beer. :-)

Tim James
+1  A: 

This kind of testing is called a Monkey test. When done right, it can smoke out bugs from the really dark corners.

To address your concerns about reproducibility: the right way to approach this, is to record the failed test entries, generate a unit test, which probes for the entire family of the specific bug; and include in the unit test the one specific input (from the random data) which caused the initial failure.

Silver Dragon
+1  A: 

If you're using random input for your tests you need to log the inputs so you can see what the values are. This way if there is some edge case you come across, you can write the test to reproduce it. I've heard the same reasons from people for not using random input, but once you have insight into the actual values used for a particular test run then it isn't as much of an issue.

The notion of "arbitrary" data is also very useful as a way of signifying something that is not important. We have some acceptance tests that come to mind where there is a lot of noise data that is no relevance to the test at hand.


Your co-worker is doing fuzz-testing, although he doesn't know about it. They are especially valuable in server systems.

Robert Gould