Are You Experiencing Technical Drift?

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Dec 19, 2013

David Byrne, in the seminal Talking Heads song “Once in a Lifetime,” sings, “How did I get here?” The meaning of those lyrics have been fodder for some great discussion, but I think there’s one interpretation that has been sorely missing from that discourse — that the narrator is a software developer. The software developer, reflecting back on his work, is wondering how he accumulated so much technical debt.

“Letting the days go by, let the water hold me down.”

The water which holds him down is an obvious reference to what can happen to a software developer over time, caught under the weight of their own codebase. But what Byrne is singing about here isn’t strictly technical debt. Technical debt is the outcome. He’s singing about something else, about “letting the days go by,” about letting your codebase slip away from you.

Byrne, although he did not know it at the time (after all, even the phrase technical debt hadn’t been invented yet) was singing about “technical drift”. Technical drift is a phenomenon that impacts a lot of well-meaning software development teams. It’s a phenomenon that occurs when a development team continuously fails to recognize and adapt to change, causing the concepts in the software’s domain and the concepts in code to start to slowly “drift” apart from one another, creating dissonance which ultimately leads to technical debt.

One of the goals of software development is to write software that most closely reflects the domain in which we’re working. This creates more understandable code that is easy to reason and discuss, both within the tech team and with the rest of the organization. But it’s important to remember that the domain is continually shifting. We’re receiving new requirements based on new information.

If we don’t adapt to these changes, a gap will appear between our domain and software, and the gap becomes our friend, technical debt.

While the signs of technical drift may be hard to spot, technical debt, the result, starts to become readily apparent.

In some cases, a software development team, or a team leader, recognizing that the gap has become too large, comes together and decides “enough is enough”. What is needed is Design — Big Design. The team stops feature development, buckles down, and refactors their code and architecture to more cleanly map to their domain.

The problem with Big Design is that it’s a “stop the world” operation. Feature development stops, and an anxious queue begins to build up waiting for the development team to free up. Furthermore, it grants false hope that such a thing might not be necessary again, but if the pattern continues, it often becomes inevitable.

How do we prevent technical drift?

If you’re not taking an active role in refactoring your application every week chances are you’re experiencing “technical drift”. Having a problem space that is continually changing necessitates continuous reworking of how your application is designed — what Agile practitioners call “iterative design”. When you’re able to do this effectively, you can round off the peaks and avoid some of the pitfalls of Big Design.

Note that the “Code” and “Domain” lines never intersect. We must accept that we do not yet have enough information to make some kinds of design decisions. But we can get close.

Understanding technical drift is more of a mindset than a particular practice, but it can help put into context why it’s important to remain vigilant, to call attention to code and domain mismatches, and to advocate for accurate and precise names wherever possible. Gaps which you’re creating may be well understood today, but as your team changes, and time progresses, that understandability suffers. Make the investment now, double down and pay attention.

Years later, you don’t want to look back in horror on your code and say to yourself “my god, what have I done?"

About
Sasha Rezvina

Why a Strategy Matters

At the heart of every successful software engineering team is a drive for three things:

1. A culture of continuous improvement
2. The ability to move from idea to impact quickly, frequently, and with confidence
3. A software organization delivering meaningful value

These goals sound simple enough, but in reality, achieving them requires more than just wishing for better performance. It takes data, action, and, most importantly, a cultural shift. And here's the catch: those three things don't come together by accident.

Step 1: Define Your Purpose

The first step in any successful engineering insights strategy is defining why you're doing this in the first place. If you're rolling out developer productivity metrics or an insights platform, you need to make sure there’s alignment on the purpose across the board.

Too often, organizations dive into this journey without answering the crucial question: Why do we need this data? If you ask five different leaders in your organization, are you going to get five answers, or will they all point to the same objective? If you can’t answer this clearly, you risk chasing a vague, unhelpful path.

One way I recommend approaching this is through the "Five Whys" technique. Ask why you're doing this, and then keep asking "why" until you get to the core of the problem.

Step 2: Understand Your People

Once the purpose is clear, the next step is to think about who will be involved in this journey.

1. Who will be using the developer productivity tool/insights platform?
2. Are these hands-on developers or executives looking for high-level insights?
3. Who else in the organization might need access to the data, like finance or operations teams?

It’s also crucial to account for organizational changes. Reorgs are common in the enterprise world, and as your organization evolves, so too must your insights platform.

Step 3: Define Your Process

The next key component is process—a step that many organizations overlook. It's easy to say, "We have the data now," but then what happens? What do you expect people to do with the data once it’s available? And how do you track if those actions are leading to improvement?

Step 4: Program and Rollout Strategy

The next piece of the puzzle is your program and rollout strategy. It’s easy to roll out an engineering insights platform and expect people to just log in and start using it, but that’s not enough.

Step 5: Choose Your Platform Wisely

And finally, we come to the platform itself. It’s the shiny object that many organizations focus on first, but as I’ve said before, it’s the last piece of the puzzle, not the first.

Looking Ahead

To build a successful engineering insights strategy, you need to go beyond just installing a tool. An insights platform can only work if it’s supported by a clear purpose, the right people, a well-defined process, and a program that rolls it out effectively.

Remember, a successful software engineering insights strategy isn’t just about the tool. It’s about building a culture of data-driven decision-making, fostering continuous improvement, and aligning all your teams toward achieving business outcomes. When you get that right, the value of engineering insights becomes clear.