# Unit testing random number generator

##### 2019-11-20 19:38

Last year I wrote a chapter for OReillys book Beautiful Testing (ISBN ). The publisher gave each of us permission to post our chapters online, and so here is Chapter 10: How to test a random number generator.Threadsafe random number generator with unit testing support in C# In a current project I have to deal with lots of multithreaded random number generation. The first challenge that I came upon was that standard. NET Random implementation is not threadsafe. unit testing random number generator

So far I have put the call in a loop so it runs 1000 times and fails the test if the number is less than 0 the number is more than 10 How can I test that all the numbers aren't just the same, i. e. First of all, create an interface for the random number generator, such that test will be able to replace it with the predefined one. If you use a random number generator in your unit test it is no longer unit. Then think about what do you really want to test. I guess, that this algorithm should be tested: May 14, 2008 The subject already says it, how would you go about unittesting a random number generator? Specifically, I've got some algorithms that generate random uniformly distributed points on and in various geometric shapes (spheres, discs, rectangles, boxes, cylinders and so on).unit testing random number generator You will not be able to 100 test the RandomSource but the suggestions you got for testing the values in this question can be applied to it (Like testing that rand(1, 26); always returns a number from 1 to 26.

## Unit testing random number generator free

I suggest you treat your source of randomness (a random number generator or whatever) as a dependency. Then you can test it with known inputs by providing either a unit testing random number generator To test that your code responds correctly, it is normal to use a low visibility method to produce the random numbers so that it can be easily overridden by a unit test class. This overridden method effectively mocks out the random number generator and gives you complete control over what is produced and when. The problem with testing randomness is that there isn't an expected value for most of the things you'd like to test. You can test with a given seed, but that only tests repeatability. It doesn't give you any way to measure how random the distribution is, or if it's even random at all. Instead what you could do is mock the random number generator and make it return nonunique numbers. This test groups the random numbers into a relatively small number of groups. The random numbers from the generator are mapped into a relatively small number of distinct integer values from 10 to 181. If the random number generator is close to truly random, then the closer our approximation should be to the actual value of. We can simplify the calculations necessary for the test if we only concern ourselves with one quadrant of the square.

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