Tuesday, 19 February 2013

Specifications, Tests & Code

This is a quick reaction to various things I've read recently, most immediately this tweet:

I think the observations in this article by Bertrand Meyer about the limits of testing are entirely correct. In any even vaguely complex system you cannot begin to test all the combinations of inputs and outputs. That's why we focus on testing what we think are the important cases and what we think are the boundary conditions. I agree with him that as such the tests are not the specification and cannot be. So I don't think we can just replace "Test" with "Spec" and solve the problem.

(I should stick in a caveat here - I read a tweet by Ben Goldacre recently saying that people who rebut tweets in blog posts (or newspaper articles) are being prats, because a tweet by its nature is going to lack subtlety and depth of argument. I dare say that Kevlin Henney would mount a staunch defence of what he actually meant, perhaps along the lines of Martin Fowler's "Specification by Example" essay which acknowledges that a specification by example will be necessarily incomplete with the rest of the specification to be inferred from it.)

I think there's a useful analogy with real science. A specification is the equivalent of a theory; F=MA, for instance, or E=mc2. A test is the equivalent of an experiment; for a given set of controlled inputs, it measures the actual output against that predicted by the theory. And the running system is the equivalent of the real world. Just as in science, the tests (experiments) cannot prove the specification (theory) holds in the runtime system (real world), they can only disprove it. A black swan event can still occur (and anyone who has ever written software will have encountered bugs in well tested software arising from inputs the tester had not anticipated and so had not tested for).

The analogy breaks down in two respects; firstly, a correct but failing experiment in science means that it's time to re-evaluate the theory, because reality isn't subject to error, whereas often in programming it means that the running system is not behaving as actually desired.

Secondly, in science the theory (specification) is something a human being writes and understands and is obviously distinctly separate from the real world (runtime system); it may or may not accurately represent it. This leads me on to the second article that prompted me to write this post; Leslie Lamport arguing that we need formal specifications in addition to code. To me a specification is a formal, logically precise, human readable statement of precisely how a system is expected to operate under all conditions. So far so in agreement. However, once you've got such a thing, I think it should be possible to compile it into a form a computer can execute, and the name for human readable text that can be compiled into a form that a computer can execute is "source code".

I do not accept at all the the notion that the specification states "what and why" and the code states "how". Code is written at multiple levels of abstraction, typically represented by functions. I would argue that the why, what and how are encoded in these abstraction layers. For any given function, the function name states "what", the context of the parent function in which it is called states "why" and the body of the function states "how". As you move up and down the call stack, these roles change.

Which I think raises the question - if the runtime system (real world) is actually compiled from the specification (the theory) and the tests (experiments) are written to validate the specification (theory) is correct, haven't we got a circular argument? How can the tests ever fail? And why do we even need them?

I think the answer is that most of the time in programming we have two levels of specification. One exists in our heads or in a requirement document or a user story; it's informal, it doesn't cover all the cases, it may even be self contradictory or downright impossible at times, but it's essentially "correct" in the sense that it captures what we actually want this system to do. That's the one we use to write our tests with. Then we have to create the formal specification of what it should actually do under all circumstances, by writing the code. Our tests are about validating that the formal specification actually specifies what we were hoping it would specify.

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