Peak Complexity

Feb 2018

With the teams I work with, we operate with the idea of peak complexity: the time at which a project reaches its highest complexity. Peak complexity has proved a useful mental model to us for reasoning about complexity. It helps inform decisions about when to step back and refactor, how many people should be working on the project at a given point in time, and how we should structure the project.

What we find is that to make something simpler, we typically have to raise the complexity momentarily. If you want to organize a messy closet, you take out everything and arrange it on the floor. When all your winter coats, toques, and spare umbrellas are laid out beneath you, you’re at peak complexity. The state of your house is worse than it was before you started. We accept this step as necessary to organize. Only when it’s all laid out can you decide what goes back in, and what doesn’t to ultimately lower the complexity from the initial point.

When you’re cleaning your house, you do this one messy place at a time: the bedroom closet, then the attic, and lastly, the dreaded basement. Doing it all at once would be utter mayhem; costumes, stamp collections, coats, and lego sets everywhere. We’re managing our series of peak complexity points to one messy floor-patch at a time.

This model works for software, too. As we embark on a complex project, we need to consider the pending complexity peaks(s). It’s completely okay to add complexity along the journey, that’s part of the job. But it’s also part of the job to manage your complexity budget. Be honest with your team about where you reside on the curve. The more complexity you add, the harder it is to onboard new members to the team. Typically, your bus factor increases, because few people can hold this complexity in their head at a time. With high complexity, the probability of error increases non-linearly. It’s prudent to review your project’s inflection points and structure it to have many small peaks. This avoids creating a Complexity Everest. A big mountain is tough to climb. It gets exponentially harder the closer you get to the top as oxygen levels decrease, wind increases, temperature drops, and willpower depletes. That’s why you want to structure your project into hills that deliver value every step of the way: day-time hikes with picnic baskets. Sometimes, the inevitable mountain appears–and that’s okay, but be realistic about what it means to the project.

The worst thing you can do is build a complexity mountain and not harvest the simplicity gains on the other side. The descent may require a smaller team and take less time than it took to climb, but is incredibly important work. As I’ve written about before, the more you can simplify the mental model of the software, the more leverage you build. If you fail to recognize peak complexity and descend you may strand there. This is how you end up supporting your project forever.

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