Refactoring

Programs consist of text that humans have written: instructions that tell the computer what to do. This text grows over years, often written by many different people. At some point it becomes confusing: the same calculation appears in five places, names no longer match their content, a section is a thousand lines long. Refactoring means rebuilding and ordering this text without changing the program’s behavior. A user notices no difference afterward. For the developers, the difference is large, because they can find their way around again.

Why software slowly becomes unusable

In software development, one speaks of technical debt. This means: whoever works quickly and sloppily borrows time from the future. Every new feature then takes a bit longer, because one first has to understand what already exists. At the beginning that’s minutes, after years it’s weeks.

Refactoring is the repayment of this debt. On the day itself it brings no visible benefit, no new feature, no new customer. That’s why it is often postponed. The price for that shows up later: teams that inherit a completely botched piece of software need an absurdly long time for small changes. Some companies eventually rewrite their software completely from scratch, which usually ends up costing more than planned.

An important distinction: refactoring is neither bug fixing nor extension. Anyone who fixes a bug while cleaning up at the same time won’t know afterward which change caused the problem if something breaks. Good developers deliberately separate these steps.

Rebuilding without tearing down the house

The comparison with a house works well here. One re-lays the wiring, tears out a partition wall, sorts out the basement. The address stays the same, the residents can keep living in it. That is exactly how refactoring works, in many small steps rather than one big leap.

Typical moves are easy to describe. A long section is broken up into several small ones with descriptive names. Code that was copied five times is consolidated into a single place that all five call. A variable named “tmp2” gets the name “remaining_balance”. None of this is spectacular, but the sum of it decides whether a program is still maintainable after ten years.

To keep this safe, one needs automated tests. These are small auxiliary programs that check whether the software still delivers the expected results. After every restructuring step, one runs them. If they fail, the last change is reverted. Without such tests, refactoring is flying blind, and cleaning up quickly turns into a new bug.

Refactoring in development teams and in AI tools

Anyone working in a software team hears the word daily. It appears in task lists, in justifications for delays, and in discussions about how much time a team gets for cleanup work. Many teams reserve a fixed share, roughly ten to twenty percent of their working time. Development environments, i.e., the programs in which code is written, have built-in refactoring functions. One click renames a variable throughout the entire project.

In recent years, refactoring has also become a selling point for AI tools. Coding assistants such as GitHub Copilot or Cursor suggest how to break up confusing sections and, on request, carry it out themselves. For small, clearly bounded changes this often works well.

A common misconception, however, is that AI thereby solves the problem. A language model sees only a portion of the project and rarely knows the reasons why a particular spot looks strange. Sometimes there’s a deliberate decision behind it, or a special case from practice. Anyone who adopts suggestions unchecked produces clean-looking code with new bugs. So even with AI, tests remain the real safeguard.