Software Maintainability in New Technologies

Every decade at the longest, the software development industry undergoes significant technological shifts, and these shifts make it difficult to keep delivering maintainable software. New technologies solve a lot of problems for users and developers alike, whether it’s new platforms (the web, mobile, wearables, VR), new programming language paradigms (new static type systems, concurrency models, and runtimes), or new deployment options (virtualization, containerization, serverless functions). But there is one major problem that, far from solving, new technologies actually make worse: the problem of figuring out how to write maintainable software.

Specifically, how can you prevent the seemingly-inevitable descent of a codebase into problems like fragility (where changes cause unexpected things to break), rigidity (where small adjustments force much larger changes), or incomprehensibility (where you can’t understand the code to be able to change it)? How can you onboard new developers to your team due to growth or turnover, ensuring they understand the code well enough to be productive—especially when the technology is new to them or new to everyone?

If you have a particular collection of the above techniques that you think are essential, I’m not asking you to give them up: I’m encouraging you to shift how you think about them. If your goal is to practice evolutionary design, think of each technique as a means to an end. This mindset opens you up to the possibility that the ideal set of techniques might be different for different individuals, different teams, different platforms, different business domains, or at different times. Separating the end (evolutionary design) from the means allows us to find more common ground and learn from one another to drive the practice of evolutionary design forward.

Distinguishing the means from the end has been the bulk of my professional journey for the past six years. I first learned about evolutionary design in the Ruby world, where dynamic typing, object-oriented design, and test-driven development were paramount. The message I got, explicitly or implicitly, was that those techniques are an essential part of evolutionary design. But since that time I’ve seen from additional perspectives: I’ve seen how static typing helps to communicate APIs to large teams, how React.js’s function-based API provides its own kind of flexibility, and how test-driven development is more costly for some types of programs and some types of programmers. In those situations I found that the specific techniques weren’t the essential thing I was reaching for; instead, the essential was “how can I evolve my code over time with confidence?”

Refactoring, Second Edition by Martin Fowler—the first two chapters in particular are an excellent survey of and argument for evolutionary design. If you’re already familiar with the literature on evolutionary design, and you feel like you’re the only one in your current technology stack who is, don’t be discouraged. Instead, look for ways to apply these principles yourself, then show others—once you’ve put in the work, others may see the benefits and get interested. You might consider rereading those classic books, not so much to learn new things as to separate specific techniques you’ve used in one ecosystem from the general principles that can apply anywhere.

Josh Justice

Big Nerd Ranch

Josh Justice has worked as a developer since 2004 across backend, frontend, and native mobile platforms. Josh values creating maintainable systems via testing, refactoring, and evolutionary design, and mentoring others to do the same. He currently serves as the Web Platform Lead at Big Nerd Ranch.

Big Nerd Ranch

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