Why does software development start out fast, then slow to a crawl? Why does this happen faster when coding with a genie? What can we do about it?
We’re going to play with graphs here for a second. The simplest way to look at our conundrum is to watch the features over time.
Rapid progress at first, then stagnation despite our best efforts. Bugs pile up. The build slows. Backwards compatibility imposes its own tax on progress. Original team members move on, while new members take time to acclimate.
What’s to be done? Is this just the price of progress?
Let’s take the first derivative of features, feature progress over time.
Starts out gangbusters, then devolves to occasional bursts of productivity. We’re no closer to a diagnosis or a treatment. We just know that coding with a genie compresses time.
The typical time series plot fails to generate understanding because it implies that the only source of change in the response is the unrelenting, impassionate drumbeat of time. Show cause. Show effect. — reader of Edward Tufte’s site.
Edward Tufte in Visual Display of Quantitative Information laments that most graphics are time series. If you want to understand the relationship between two variables, you need to plot them with each other, not just as part of the same timeline.
We want to understand feature development, so that needs to be one of the axes. I’m going to put it on the horizontal axis, for reasons that will become apparent (I tried it every which way before settling on this—I’m giving you the abridged version).
If all we’re looking at is feature progress, we expect progress to be spread apart (fast) at first, then bunch up as time passes.
But what goes on the vertical axis?
No big surprise here, my fellow Tidiers. We know that software embeds economic value 2 ways:
Cash flow from current features
Optionality for future features
Let’s see how this plays out. We start out with no features & lots of options:
The first feature we develop goes pretty far but inevitably it burns options.
The code is now more complicated, we have to either preserve backwards compatibility or tear out the feature before proceeding.
(I hear the “why not make it better on both axes at once?” crowd. Make it run then make it right. My brain isn’t big enough to do a good job of both at once. Not even my augmented brain.)
Okay, so we have one feature. We want more! Features feel good! Features make customers feel good!
It wasn’t much harder than that first feature. What’s that you say? More features & then more features? You got it!
You got it right until you don’t. If you look at my GitHub repos you’ll see BPlusTree, BPlusTree2, BPlusTree3, etc. In each case the genie & I drove the project out of options & had to start over. Somehow, though, I don’t learn my lesson. I don’t name the first project BPlusTree1—this time will be different!
What’s the alternative? We can’t improve both axes at once. We can’t escape burning some options with each feature. The first feature is the same.
We do have a choice when we’ve finished that first feature, though. There is a space between features, should we choose to perceive it. In that space we can choose to invest in restoring & enhancing optionality.
Tidying first is one way to perceive this gap between features. What’s the next feature we are implementing? What about our current mess makes that harder? What can we tidy to make it easier? If implementing the feature is easier, then the option of implementing it becomes more attractive.
We can, should we so choose, continue this rhythm—feature, options, feature, options.
That’s all I have time for today. I’ll use this same “Options Lead To Features” graph to describe a variety of problematic situations in development, augmented & not.
Thank you to Gene Kim for a long series of Tokyo breakfast conversations (long story) that helped refine this diagram.
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