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Asked: 2026-05-21T04:59:53+00:00

I have asked a related question , but I did not get a satisfactory

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I have asked a related question, but I did not get a satisfactory answer. So, perhaps I should ask it a different way.

How do large-scale C projects, like Perl or Ruby or even the Linux kernel, handle unit-testings? Or even in any functional language?

I am familiar with Dependency Injection and Abstract Factory for testings in OOP, but I don’t see a scalable and manageable equivalence in non OOP. In C or Haskell, for example, there would be layers over layers of functions, higher ones implicitly calling the lower ones. How do I find seams to test just an unit of code instead of all its dependencies?

One way to avoid the need for seams all together is to keep the depths of call-dependency graph very low. Code horizontally rather than vertically, so to speak. Keep as much of the application logic as possible in the “leaf” functions; and make sure the “node” functions do no work other than plumbing the data to other node/leaf functions. Then, test only the “leaf” functions; leave the “node” functions out to integration tests. Is that approach effective?

The largest software today are still written in procedural languages. There must be some methodologies being employed that work. Could someone with experience with large-scale software in procedural languages with good unit-testings comment?

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  1. Editorial Team

    Functional languages have other constructs (than objects) for modularity, such as ML functors. “Dependency injection” is basically a glorified name for “abstracting over things” and has been used for ages in functional languages.

    Testing, in all paradigms, should follow the specification boundaries. If you have an idea what a given piece of code (function, method, object…) is supposed to do, you should test against this specification. For leaf functions, this will be unit testing, for “node” function this can be considered “integration testing” if you like, but it’s really the same activity.

    I think you will find that the same methodologies essentially applies to functional programming, with essentially the same results; in particular, (re)designing code to be easy to test also improves its modularity and maintainability.

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