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Home/ Questions/Q 979565
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Editorial Team
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Editorial Team
Asked: May 16, 20262026-05-16T04:14:22+00:00 2026-05-16T04:14:22+00:00

I’m talking about a large scales system, with many servers and non deterministic input

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I’m talking about a large scales system, with many servers and non deterministic input in high capacity. When i say non deterministic i’m talking about messages that are sent and you catch what you can and do the best you can.
There are many types of messages, so the input could be very complicated.
I can’t imagine writing the code for so many scenarios and a simple non random (deterministic) message’s generator is not good enough.

That’s why i want to have a randomized unittest or server test that in case of a failure could write a log.

And i prefer the unittest instead of a random injector because i want it to run as part of the night build automated tests.

Any downsides?

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  1. Editorial Team
    Editorial Team
    2026-05-16T04:14:22+00:00Added an answer on May 16, 2026 at 4:14 am

    Downsides

    Firstly, it makes the test more convoluted and slightly harder to debug, as you cannot directly see all the values being fed in (though there’s always the option of generating test cases as either code or data, too). If you’re doing some semi-complicated logic to generate your random test data, then there’s also the chance that this code has a bug in it. Bugs in test code can be a pain, especially if developers immediate assume the bug is the production code.

    Secondly, it is often impossible to be specific about the expected answer. If you know the answer based on the input, then there’s a decent chance you’re just aping the logic under test (think about it — if the input is random, how do you know the expected output?) As a result, you may have to trade very specific asserts (the value should be x) for more general sanity-check asserts (the value should be between y and z).

    Thirdly, unless there’s a wide range of inputs and outputs, you can often cover the same range using well chosen values in a standard unit tests with less complexity. E.g. pick the numbers -max, (-max + 1), -2, -1, 0, 1, 2, max-1, max. (or whatever is interesting for the algorithm).

    Upsides

    When done well with the correct target, these tests can provide a very valuable complementary testing pass. I’ve seen quite a few bits of code that, when hammered by randomly generated test inputs, buckled due to unforeseen edge cases. I sometimes add an extra integration testing pass that generates a shedload of test cases.

    Additional tricks

    If one of your random tests fails, isolate the ‘interesting’ value and promote it into a standalone unit test to ensure that you can fix the bug and it will never regress prior to checkin.

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