Unit Test Case, Subject Matter Experts and Requirements
Here's a typical "I don't like TDD" question: the topic is "Does TDD really work for complex projects?"
Part of the question focused on the difficulty of preparing test cases that cover the requirements. In particular, there was some hand-wringing over conflicting and contradictory requirements.
Here's what's worked for me.
Preparation. The users provide the test cases as a spreadsheet showing the business rules. The columns are attributes of some business document or case. The rows are specific test cases. Users can (and often will) do this at the drop of a hat. Often complex, narrative requirements written by business analysts are based on such a spreadsheet.
This is remarkably easy for must users to produce. It's just a spreadsheet (or multiple spreadsheets) with concrete examples. It's often easier for users to make concrete examples than it is for them to write more general business rules.
Automated Test Case Construction
Here's what can easily happen next.
Write a Python script to parse the spreadsheet and extract the cases. There will be some ad-hoc rules, inconsistent test cases, small technical problems. The spreadsheets will be formatted poorly or inconsistently.
Once the cases are parsed, it's easy to then create a Unittest.TestCase template of some kind. Use Jinja2 or even Python's string.Template class to rough out the template for the test case. The specifics get filled into the unit test template.
The outline of test case construction is something like this. Details vary with target language, test case design, and overall test case packaging approach.
t = SomeTemplate()
for case_dict in testCaseParser( "some.xls" ):
code= t.render( **case_dict )
with open(testcaseName(**case_dict ),'w') as result:
result.write( code )
You now have a fully-populated tree of unit test classes, modules and packages built from the end-user source documents.
You have your tests. You can start doing TDD.
One of the earliest problems you'll have is test case spreadsheets that are broken. Wrong column titles, wrong formatting, something wrong. Go meet with the user or expert that built the spreadsheet and get the thing straightened out.
Perhaps there's some business subtlety to this. Or perhaps they're just careless. What's important is that the spreadsheets have to be parsed by simple scripts to create simple unit tests. If you can't arrive at a workable solution, you have Big Issues and it's better to resolve it now than try to press on to implementation with a user or SME that's uncooperative.
Another problem you'll have is that tests will be inconsistent. This will be confusing at first because you've got code that passed one test, and fails another test and you can't tell what the differences between the tests are. You have to go meet with the users or SME's and resolve what the issue is. Why are the tests inconsistent? Often, attributes are missing from the spreadsheet -- attributes they each assumed -- and attributes you didn't have explicitly written down anywhere. Other times there's confusion that needs to be resolved before any programming should begin.
The Big Payoff
When the tests all pass, you're ready for performance and final acceptance testing. Here's where TDD (and having the users own the test cases) pays out well.
Let's say we're running the final acceptance test cases and the users balk at some result. "Can't be right" they say.
What do we do?
Actually, almost nothing. Get the correct answer into a spreadsheet somewhere. The test cases were incomplete. This always happens. Outside TDD, it's called "requirements problem" or "scope creep" or something else. Inside TDD, it's called "test coverage" and some more test cases are required. Either way, test cases are always incomplete.
It may be that they're actually changing an earlier test case. Users get examples wrong, too. Either way (omission or error) we're just fixing the spreadsheets, regenerating the test cases, and starting up the TDD process with the revised suite of test cases.
Interestingly, a bug fix after production roll-out is no different from an acceptance test problem. Indeed it's no different from anything that's happened so far.
A user spots a bug. They report it. We ask for the concrete example that exemplifies the correct answer.
We regenerate the test cases from the spreadsheets and start doing development. 80% of the time, the new example is actually a change to an existing example. And since the users built the example spreadsheets with the test data, they can maintain those spreadsheets to clarify the bugs. 20% of the time it's a new requirement. Either way, the test cases are as complete and consistent as the users are capable of producing.