Thursday, June 12, 2014

TDD, API Design and Refactoring

See this short discussion on a Stingray Reader feature:

This turned into an exercise in pure TDD.

I'm not a fan of applying TDD in a strict, death-march fashion.

I see the comments on Stack Overflow that indicate that some folks feel strongly that strict TDD is somehow helpful. While "test before code" is laudable and often helpful, there's no royal road to good software.

Design involves a great deal of back and forth between code and test. A great deal.

It's logically impossible to write a test without having thought about the code. In order to write the test first, there must be a notional API against which the test is written. Anyone who requires that the test file must be written before the notional class or module is just playing at petty tyranny.

The notional design -- the rough outline of the class or module -- can be written into a file before any tests. It's okay. It is still test-driven because the considerations of testability drove the design process.

In particular, when starting "from scratch" -- with nothing -- writing tests first is senseless. Some module or package structure must exist for the test modules to import.

Having ranted, it still arises that the tests do come before any code under some circumstances.

In this case, the requested functionality was quite difficult to visualize. However, it was possible to cobble together a test case that simplified the problem down to something like this this:

01 Some-Record.
     05 Header PIC XXX.
     05 Body PIC X(17).

01 ABC-Segment.
     05 Field-ABC PIC X(17).

01 DEF-Segment.
     05 Field-DEF PIC X(17).

In COBOL, the program would use logic like IF Header EQUALS "ABC" THEN MOVE Body TO ABC-Segment. We need a way to handle something like this in Python so that we can parse the EBCDIC COBOL data.

This summarized example allowed construction of a test case that made use of a API that might have existed. I was pretty sure I had a test case that showed an approach.

What Actually Happened

Since the application already had 178 unit tests, there was plenty of structure that worked.

The single new unit test relied on a notional API that wasn't really in place. The new test bombed grotesquely.

There are two solutions:

  • Modify the test.
  • Fix the notional API so that it works properly.

I started out chasing the second option. I tweaked some things. More tests failed. I tweaked some more things. The new test finally passed, but another test was failing.

Some careful study of the failing test revealed that my approach was wrong. Way wrong.

The notional API was a bad idea.

The tweaks to make it work were a worse idea.

Back to the Lab Bench

At this point, I had made enough changes that the only thing to do was copy the new test and use the Git Revoke on the local changes to unwind the awful mistakes.

Staring again, I had a slightly better grip on the relevant code. I had a failing test. I tried a different approach that wasn't quite so inventive. This meant modifying the test.

I actually went through a few iterations of the test, using the test method as a kind of lab bench.

A more Pythonic approach to the lab bench is to work from the >>> prompt. I think that all of the exemplary projects use the >>> prompt examples in their documentation. This is a way to narrow and clarify the API. As projects get big, they can sprawl. New features can wind up with many imports to pick and choose elements from existing modules.

When it becomes difficult to use the >>> prompt as the lab bench, that's a sign that the API is too complex. Refactoring must happen.

Using the unit test framework as the lab bench was a hint that something had drifted out of tolerance.

However. I did get a test which passed. Yay. Sort of.

The test code was hideous.

TDD and API Design

The point of TDD, however, is that we have a working suite of tests. Refactoring won't break anything.

The point was that the hideous API could be rewritten into something that both

  • Passed all the tests, and
  • Was usable at the >>> prompt.
It's difficult to express how valuable the Python >>> prompt is to help clarify API design issues.

The rule is this:

If the API doesn't make sense at the >>> prompt, it's incomprehensible

Sadly, Java doesn't have this kind of boundary. Java programming can spin into quite complex API's, limited only by the laziness of the programmer who avoids refactoring.

Or the malice of the programmer's manager in not allowing time to refactor.

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