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Iteration Two: Refactoring and Minimax

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I’m just about finished with my second iteration as an apprentice at 8th Light. Where my first iteration centered a lot around reading and learning how TDD and Ruby work, this week I focused more on coding. I spent most of my time just implementing the several stories of my iteration: some minor (but important!) output tweaks, implementing a play again loop, and adding two AI’s to the game. I also did quite a bit of refactoring and general code cleanup.

I spent several hours figuring out the mechanics of doing an extract class refactoring, and then realizing that I had extracted the class the wrong way and so the “container” class was the one being held by it’s containee. It turned out to be very awkward to try to invert this relationship so I backed out the change and extracted the other class so that the container/containee relationship was correct. I consulted Martin Fowler’s Refactoring book heavily here. It’s really neat how the mechanical steps he describes are actually an incredibly useful guide.

I noticed an interesting thing while I was refactoring. I was changing the code so much that I started losing track of what was actually going on. At some point, my brain just sort of gave up on trying to hold the whole application in my head. This was an interesting experience because usually I can hold more complexity in my head than this Tic Tac Toe game currently has. I think this is part of the trend I noticed that in general, when doing TDD I feel less need to hold a lot of the complexity in my mind at once.

The slightly scary flip-side of that was that because I was doing extract class refactorings and moving methods and such, I also ended up moving tests around and modifying the tests a lot to keep them working. Mucking around with them seriously decreased my confidence in them, and thus in my confidence in the system as a whole. Clearly I need to think more about how the test modification process goes along with refactoring.

Another piece of functionality that took me surprisingly long to implement was the play again functionality. The interesting part of that process was that I wrote tests, and then wrote an implementation that I thought was correct. The tests failed in a confusing manner and so I assumed that the tests were incorrect. After poking around for quite a while, firing up the debugger (Pry and byebug are awesome!) I eventually discovered that the test was correct, and it was even failing exactly correctly. I had simply forgotten to take into account that once a game has been completed, one can’t just start playing it again from the beginning. So I had to write a reset method.

On Tuesday I spent most of the day test driving my way to a perfect AI. This was an interesting experience since it was the first time I’ve had a really clear idea of where I wanted to go with the code while doing TDD. For the most part I’ve been trying to stick to the letter of the acronym and let the tests lead me to the code organization they want.

But the minimax algorithm is well known and an established way to do the game state-space search that is required for a perfect Tic-Tac-Toe AI. So how does one test drive that? Apparently I am most definitely not alone in asking this question as both my mentors were expecting it, and my fellow apprentice working on Tic-Tac-Toe (Ari) also spent time last week puzzling over it.

I worried about it briefly at the start of the day, but then I quickly adopted the outlook that I’ve found to be most successful with TDD; don’t think too much about the future. Instead, I focused on the fundamentals. What’s the simplest case an AI needs to deal with. I turned to the Wikipedia page on Tic-Tac-Toe and it’s bullet list of how to be a perfect player. The most important rule is that an AI should win immediately when it can. If that’s not possible it should block an opponent from winning.

These two situations are very easy to detect. It only takes a slight modification to the code pattern used for detecting if someone has already won, and no future board states need to be taken into account. This approach got me moving forwards, which is often the hardest part.

Once I had some momentum I kept going by then considering simple fork creation, the next most important move type. After pondering how this might work, I hit upon a simple solution. A fork is just a move that results in a board where you have two possible ways to win! This was attractive to me for several reasons. First, it was a (relatively small) step towards searching the state-space of the game. By looking one move ahead I felt I was moving in the direction of minimax. And it was simple enough that it felt doable in a single TDD step.

This worked shockingly well. Not only did my very simple fork look-ahead detect most forks that it could create, somehow it actually was able to produce the correct fork-blocking behavior in the first four cases that I came up with.

I was suspicious though, and so I crafted some devious fork tests that were designed to expose the flaw in my simple algorithm. Eventually I did that and it became clear that something more powerful was needed. At this point, thinking about the simplest way to do things produced no clear results. I was already doing a minimal look-ahead and the analysis was insufficient. It seemed that the next step would have to be going to a minimax algorithm. Here’s basically what my simple algorithm was at this point:

  def score(node)

  def find_forks(board)
    scores = board.empty_spaces.map do |index|
      node = board.speculative_move("X", index)
      [index, score(node)]

    scores.max_by { |i, s| s }.first

  def get_move(board)
    attacks = board.indexed_attack_sets
    win   = get_wins(attacks).first
    block = get_blocks(attacks).first

    win or block or find_forks(board)

I tried to do get to minimax in several ways. First off, I actually ended up using the negamax algorithm because it’s simpler to code because it takes advantage of the zero-sum property of Tic-Tac-Toe. Initially, I tried to replace my whole algorithm with negamax right off the bat. This did not work out well. Things got complicated and many tests were failing. So I backed out and treated negamax as a small refactoring. Specifically I treated it as a refactoring of the find_forks function. This worked quite well, and suddenly all of the forks tests were passing. I then incrementally expanded it until negamax was the only thing being used for deciding which moves to make.

Clojure TDD

I also spent some time pairing with both Emmanuel and Kristen on some Clojure katas, the bowling game and coin changer respectively. This was really fascinating for a couple of reasons. I consider myself reasonably competent with Clojure. But I am almost totally unfamiliar with trying to do TDD in Clojure.

The pairing that I did with both my fellow apprentices was in the Ping-Pong style (mostly), where one of us wrote a test and the other tried to make it pass. What was fascinating to me was that the incremental development approach that this enforces pushed me towards writing very non-idiomatic, highly stateful Clojure code.

After attempting the coin changer with Kristen, I was starting to doubt my Clojure-chops and so I tried to TDD it out on my own. I ended up arriving at a more satisfactory solution that used a stream model of discrete state transformations. So a cleaner stateful solution.

I ended up talking about this with Emmanuel after our pairing session, and he showed me a blog post and a screencast about doing the bowling kata in Clojure. Both people ended up with similar solutions, which basically involved the insight that a frame in bowling is only two rolls when no strikes or spares are involved. In retrospect, this seems sort of obvious, since a frame can’t be scored until all of it’s rolls have occurred. But combining this with actually duplicating some of the numbers from the stream of pin-counts is what leads to an elegant Clojure solution.

Besides this key realization, I think I was also missing a critical tool in my Clojure toolbox: custom lazy sequences. Both solutions Emmanuel had found on the web ended up constructing a function that produced a custom lazy sequence. This was necessary because of the need to repeat certain elements from the stream.

But what I’m realizing now is that I think it’s difficult to test drive recursive solutions. But maybe I’m just doing it wrong. Often when I write recursive functions I don’t bother to write just the base case first. I typically will just jump straight in and write both parts at once. But now that I think about it, testing the base case of a recursive algorithm would be really natural in TDD. You just have to start at the simplest case.

Another thought that occurred to me about why TDD in Clojure is harder. In Ruby or Java I have read about different design patterns and there are established refactorings for manipulating class-based OO code. So when I’m doing TDD in those languages I know the patterns of it. Not patterns in the design patterns sense, but the flows of code configurations you might say. Specifically, I’m familiar with the intermediate stages and I can imagine them.

But the way I write functional code is somewhat different. Most of the time, I experiment with a small piece of code in the REPL, until I’ve produced the result I want, then I wrap it up into a function. This process can proceed top-down where I conceive of the top-level function first and then create the necessary pieces to make that happen; or it can go bottom-up where I think about little pieces of functionality in the domain I’m considering, and then start composing those pieces to make more complex things.

In both of these styles of development, there isn’t always working code at all times though. In particular, the interesting sort of high level behaviors often don’t work until the whole thing comes together at the end.

So I think I need at least two things before I can do TDD in Clojure more effectively. I need to learn the patterns of Clojure code better, and get more familiar with how it can be molded and changed from simple and ill-factored to cleaner and well-factored. And second, I need to adjust to the paradigm shift of thinking about code tests first in a functional context.