Thursday, November 14, 2019

Refactoring: Reads Before Writes

One of the advantages of practice coding is that it gives you permission to pause your progress and investigate process smells; did I just make a mistake?

Today, I was working on a refactoring exercise; I had code, and tests in place, and all of my tests are passing.  I made a change, ran the tests, and tests failed.  Startled, I reverted the change.  The tests are now passing again.

But I had to stop here, because I happened to notice a problem: the change that I reverted to pass the test was correct.

What had happened?  Well, that's easy -- one of my earlier edits included a mistake, because of the mistake, the behavior of the code was incorrect.  But the test didn't detect the mistake at the point that it was introduced.  Instead the test appeared later.

This concerns me, because one of the illusions that I carry around with me is that there is a payoff in TDD that you spend less time in the debugger because mistakes are caught when you make them.   Test-commit-revert is, to some degree, founded on this idea -- that you can discard the mistake by simply reverting the code.

Assuming, for the moment, that all other things are equal, I prefer habits that produce short intervals between the mistake and its detection to those that produce longer intervals.

So, what happened today?


I was working on a greenfield project; write a test, make it pass, clean it up, repeat. And my "imagined design" was not expressed in the code. The changes I want to introduce appear to be getting harder, so I'm interrupting my "progress" to make the next change easy.

My imagined design is a finite state machine, and I'm trying to organize the code in a way that introducing a new state (with new behavior) will be easy.

I'm writing a game, and tracking the token as it moves from the starting square to the second square; the tests verify the descriptions of the squares against a fixed transcript.  The test that I am anticipating moves the token back to the original square, with a different description than was initially displayed.

My thought was to introduce a predicate which is always true (preserving the current behavior), and then to introduce the new test which would pass if the predicate were false.  Thus, I would meet one of my goals: minimizing the time required to complete the "Green" task.

And, having practice that approach several times this morning, it works fine.

But not the first time that I tried it.

The first time through, there were two differences in the technique that I used which both contributed.  One difference was that I typed my code, rather than using the refactoring tools.  That made it possible to introduce an error - a line of code I thought was assigning a value to a variable was in fact a no-op.

The other difference is that I started out my refactoring by introducing the writes; creating the new variable, assigning the data to it.  I introduced the error during this phase, but because the variable is not yet being read anywhere, the coding error I've introduced has no impact at all on the behavior.  So all of the tests continue to pass.  When I introduced the new variable into the predicate, now the bug appears.

On the other hand, on the interations where I started from "If True", mistakes were detected at the moment I introduced them.

It felt a bit like TDD as if you meant it: if True is trivially correct, then true is rewritten into an equivalent comparison between two literal values, not we extract variable on one of those literals, and then that variable can be moved around in the source code.  At each mistake, a simple revert/undo would take me back to not merely a passing state, but actual bug free code that passed its tests.

Another way of considering this lesson is that we should start as close to the actual constraints of the tests, and work backwards from there, gradually moving our changes into the parts of our implementation where we have more freedom.

It's similar in nature to starting with a hard coded return value, and then removing duplication as your refactor your way toward the function arguments.

Sunday, November 10, 2019

TDD: Transport Tycoon, Exercise 1


I decided that I wanted to take a quick swing at this as a TDD exercise...

Because what we are being presented is the behavior of some pure function, I used a facade as my test subject -- a single function accepting a string argument and returning an integer.  Behind that facade I can refactor my way towards a fully buzzword compliant domain model, but these behavior tests aren't coupled to the model.

That means, incidentally, that these tests aren't some beautiful enduring artifact that describes the model in exquisite detail; they are disposable scaffolding tests.

Because I wasn't sure where I was going, I simply implemented everything in a straight forward way within the facade.  I went three examples in with `if` statements before I started trying to tease out the implicit duplication of the model.  Eventually, all three branches turned into the same code, and then they were simplified to remove that duplication.

It became clear in working on some of the longer problems that I really wanted to have more confidence in the intermediate state.  That insight led me back to the idea that I wanted a "pure function" that could handle each piece of cargo one at time, so that I could track the evolution of the system at each step.  In theory, such a thing can be created via refactoring, but since I wanted confidence in the implementation, I decided to perform a separate TDD run on just that piece, and then verified that the tests against the original facade continued to pass when I applied the refactoring.

When that refactoring was complete, the implementation behind the facade was broken into two pieces -- a state machine to manage the bookkeeping of my "fleet", and pure function that computed transitions from one state to another.

I deliberately declined to maintain CQS discipline; separating the queries from the commands in my bookkeeping component appeared to be ceremony with no particular payoff in the exercise.


Friday, October 25, 2019

Following a better path

I intend to follow a better path, now that @marlenac has brought it to my attention.

When you are able to condense your software expertise into a practice that takes <10min and a relative beginner can mimic, and repeating that exercise thousands of times brings fresh insight to everyone at every level, you can call it a code kata. -- @jtu

The good TDD exercises do offer fresh insights at many levels, but less face it; the creation of these exercises doesn't demonstrate a lifetime of mastery so much as a clever idea that survived a few rough drafts.  Maybe.

The time limit is a really interesting constraint -- much of my complaints with the TDD exercises that I've found is that the scope of them is much too brief; that an hour of exploration isn't enough time to have the problems that TDD purports to solve.


 

Friday, October 18, 2019

Refactoring Lessons at the Gilded Rose

The month, the local meetup took on a variant of the Gilded Rose kata.  As I was in a facilitator's chair,  I didn't get to explore any new ideas this time around.  So I decided to work through the exercise this evening.

In doing so, I picked up a couple interesting tricks from Intellij IDEA.

First, I learned that IntelliJ understands the idea of "run all tests in this package", which saves some of the headache of creating a regression suite when your tests are spread out across multiple files.

Second, I learned that IntelliJ can be quite clever about reducing predicates if it has a clear idea which invariant holds.  In various methods I introduced, an assert that described the preconditions for the Strings that were in scope allowed intellisense to remove a bunch of redundancy in the conditionals.

Either it wasn't entirely clever, or I made some bad choices in chosing which refactoring option to take when presented with a choice, but in a number of cases I ended up with ifs in front of empty blocks.  IntelliJ was also able to perform the refactoring to remove them, but I had to ask.


The grand strategy wasn't quite what I had expected it to be.  Back in the "Look, Ma, no hands" era, we tended to focus on micro refactorings, from which some Platonic ideal design would eventually emerge.  But for the Gilded Rose problem, the game seems to be to origami the code into a shape that intellisense recognizes.  So that means a bit of preparatory judo followed by swinging large blocks of code around so that the static code analysis can consider one domain problem at a time.

In short, instead of chasing a good design, I'm first chasing a design that the machine understands well enough to manipulate and simplify without requiring that I type anywhere near the complexity.  Let's face it, the carbonware is barely qualified to type at all -- its role is to point, and let the sand do the dirty work.

Tuesday, October 1, 2019

TDD: Safety in Numbers - a Bowling Game Adventure

Last night, I decided to work through a bowling game exercise, but it didn't quite turn out as I had expected.

The goal was eliminate duplication; how much intention revealing code could I introduce before moving onto the second test?

As suggested by Uncle Bob, I started with the degenerate case:

It is, of course, trivial to get this test passing. We simply hard code the required answer into the score method.

The step that I expected to follow was to immediately start introducing domain concepts, like frames, into the production code while there was still but a single test constraint in place.

And it was a very uncomfortable experience - I realized fairly quickly that a simple pass signal wasn't enough to give me confidence that the match I was introducing was actually manipulating the figures correctly -- not enough to give me confidence that I wasn't introducing silly fence post errors.

After discarding the work and contemplating the ceiling for a time, I decided that I was having trouble because zero is the additive identity -- I couldn't look at my actual result and deduce how many numbers had been added together, because 10, 20, 100 zeros all sum to the same amount.

This evening, I tried a different initial test:

The results are much better - fence post mistakes change the observable behavior in this circumstance, and are therefore easy to catch. The deviations from the expected results give an immediate hint at the error. We know from taking small steps which edit introduced a fault, but the distinct behaviors mean that it is easy to recognize the precise nature of the fault.

In this evenings ending, I managed to get all the way to make the next change easy: using the gutter game as my second test produced a trivial pass, because the faults I introduced during refactoring had already been detected and mitigated.

Saturday, September 7, 2019

TDD: On Fake Code

This past spring, David Tanzer published a short essay on transitioning from fake implementations to real ones.
When you do TDD, “fake implementations” or “wrong code” are OK, as long as they pass all the tests you have so far
But when do you stop to fake? When do you start writing “real code”?
Tanzer is using this as a stepping stone to introduce Uncle Bob's heuristic: as the tests get more specific, the code gets more generic.

But there is another answer, which I eventually learned from a comment written by Kent Beck:
Do you have some refactoring to do first?
Here is Tanzer's passing implementation:
And that's fine for our test calibration; we have successfully demonstrated that the test can distinguish the correct behavior from an incorrect behaviod in this specific case.

But... the current implementation implicitly describes two pieces of domain knowledge that we can make explicit.
  • The length of the hint should be the same as the length of the secret word.
  • The initial representation of the hint should conceal all of the letters in the secret word, which is to say it should be entirely composed of the unrevealed letter token "_".
We don't have to wait for permission to introduce these ideas; they are always going to appear in a refactoring step, so we can cut to the chase and introduce them immediately.

From there, we might notice that the secretWord we are using in the hint method is the same that was passed to the constructor, and extract that duplication. Or we might decide that the creation of the hint of the correct length is a single idea that can be extracted into another function, and do that.

You can start writing the real code as soon as you have a green bar.

Because I was reviewing Saff and Boshernitsan today, I have been thinking about Beck's Money demonstration.  Translated into Python, Beck's first test looks like

Riddle: what's the simplest implementation that will pass this test? There are probably several different answers, but the simplest I can come up with looks like:

No implementation, no variable names. Just 10. It's clear to me that this is "wrong code", in Tanzer's sense. But we don't need more tests to make it better, we can immediately refactor (in Beck's sense) to restore sanity to the implementation.

If we were being very small and deliberate in our refactoring, the refactoring sequence might look like:
Like "triangulation", small and deliberate steps are not required - they are a technique to practice so that you can get small when larger steps aren't working.

Monday, September 2, 2019

Thoughts on an Acceptance Tests

In my recent experiments with Hunt the Wumpus, I started thinking about what an "acceptance" test might look like.

To get started, I reviewed the walking skeleton example in Growing Object Oriented Software.  Freeman and Pryce wrote that the initial iteration should include delivery of a completely automated checkout/build/deploy/test pipeline, front loading the work of solving a number of critical system and political issues.  The acceptance test, in their example, launches the application and uses the user interface to probe and measure the app.

For an interactive shell app like Wumpus, the test harness is relatively straight forward; we control stdin to pass data to the app and control stdout to read data from the app.

What I struggle with, at this point in the narrative, is the amount of work required to create stable acceptance tests.

A point of view: automated checks are mistake detectors.  They don't provide value to the user - you can delete all of your automated checks and the behavior of your production code doesn't change.  Economically, the justification for the tests is that they reduce the costs of future work.  More precisely, we adopt processes that shutdown when a mistake is detected, ensuring that the mistakes cannot be overlooked, and that we don't expose our test subjects to expensive evaluation when the more cost effective checks have already detected problems.

There's another potential benefit to checks, which the TDD ritual seeks to exploit: thinking about how the checks will validate the behavior of your application creates space to discover important ideas in your app before you start coding it.




The acceptance tests, what with all the work we need to do to set them up, are expensive relative to other mechanisms for checking the correctness of the program.  In the case of the Auction Sniper, those tests included measuring that the app could talk to other processes.

In the case of Wumpus, there really aren't other processes to talk to unless we choose a particularly contrived design.  Only the interface to the user is interesting.  So there isn't a lot of complexity that
needs to be evaluated from the outside.

Which is good, because that evaluation is painful.

Wumpus has three awkward aspects to it; hidden information, non-deterministic behavior, and message schema.

The hidden information aspect is what introduces uncertainty in the game - with complete knowledge of the hazards in the maze, the game can be won trivially by shooting the wumpus in its lair.  But without that hidden information, one cannot know the correct outcome of any action by the hunter.


The location of the hazards in the game is non-deterministic - that's part of the mechanism for hiding that information from the player.  In addition, each of the hunters actions can induce random behavior by the hazards in the game.  These random effects mean that any given action by the player can have multiple candidate responses, depending on how the dice fall.

The feedback from the game to the player is all via messages written to the console.  Those messages were designed (such as it is) for human readability, rather than machine readability.  Understanding the semantics of those messages requires introducing a parser into the acceptance test.

What this means is that we have some work cut out for us if we want anything more than a trivial verification that some message was written to standard out.

One possibility is that we can introduce the idea of specifying a seed for the non-deterministic behavior from outside the program.  The acceptance test can fix the seed, then perform a domain agnostic comparison of the output to some golden master that we specify.  This is somewhat brittle: the current mapping of random values to representations is arbitrary, and the domain agnostic match over fits the representation of the messages.

Another possibility is to introduce an affordance that allows the specification of a message schema to use; the acceptance test simply switches the application into a mode where the responses are easy to parse, much like an http request might distinguish between text/plain and application/json.  Even without fixing the seed, our acceptance test can still easily identify that all of the messages are well formed.

The schema approach, while straight forward, feels like a lot of work that will not pay off.  I think the issue here is that, while wumpus is a more interesting toy exercise than the bowling game or a Fibonacci calculator, it is still fundamentally a toy problem -- one with an arbitrary and limited scope.

My null-design port of Wumpus from basic to Java is only 375 lines long; it's hard to envision that project having a lifetime that justifies heavy upfront investment in acceptance tests.

What we can do, from the outset, is decide that the behaviors that the acceptance test needs to control - the random seed, the interpretation of the random values, the message schema - can be controlled from the outside, and that the idiom for changing those behaviors in the future is to extend the application with new selectable behaviors, rather than replacing the existing behaviors.