If you write software for your research, you have most likely had the experience of looking at your code and realizing it has become a tangled mess. Perhaps it has even gotten to the point where you, the original author, have a hard time remembering how all the pieces fit together. Don’t despair! This is perfectly natural in research software; it is just time to refactor.
What is Refactoring?
Code refactoring is the process of restructuring existing computer code without changing its external behavior in order to make it easier to understand, reason about, or extend. Much has been written about refactoring in software engineering and computer science. The term itself has been in use since at least the early 1990s, and the canonical reference, “Refactoring: Improving the design of existing code” by Martin Fowler, was published in 2001. Fowler even maintains a catalog of the specific types of code transformations that are considered refactorings. (Note that though I use the term “refactoring” in this post, the contents here also apply to a broader set of code cleaning and restructuring techniques that go beyond these atomic code transformations.)
Why Is Refactoring Important for Researchers?
On the surface, it can seem hard to justify spending time working on your code only to have it do the exact same thing at the end of the day. Imagine being a student and explaining to an advisor that you have produced no new results but rather have been spending your time making nebulous “improvements” to your code! However, such code restructuring is an investment that will make future development easier. The longer the lifetime of a piece of software and the more people that need to read and modify it, the more important refactoring becomes. In fact, I argue that computational scientists in particular should view refactoring as a valuable and even inherent part of research software development. There are two reasons for this:
- Scientists often don’t know what the code they are writing is supposed to do before they write it. For example, when reducing or modeling data, there may be peculiarities in the data that only become apparent after some analysis. The model or algorithm must then be improved to better describe the data or otherwise deal with the peculiarities. New requirements often mean that the way the code was originally structured is no longer optimal, and the code should be restructured rather than tacking on new functionality to an existing structure that wasn’t designed to handle it.
- Research software often implements novel algorithms or applies existing algorithms to new problems. Software readability is always important, but this aspect of research software makes readability paramount: the fine details of algorithm implementation are very often important, and unless you have very detailed documentation, the only way to understand exactly what the code is doing is by reading it. If you are working in a collaboration or publishing your code, maximizing understandability will pay dividends in the long term: other researchers will be more likely to reuse the code rather than throwing up their hands and starting over because they can’t understand it. Even if your future self is the only one who will read the code, it can be a good investment now to spend time improving readability so you don’t throw up your hands and start over.
In research collaborations, the cost of not refactoring is often paid by those who inherit responsibility for a piece of code. For example, a graduate student and I took over responsibility for some software that had been developed in our collaboration over the course of about three years. The code had grown to about 20,000 lines and included many unused code paths that had been relevant at some point in development but were now only hindering understanding and preventing us from making necessary improvements. After several months of work spent understanding the code and refactoring it, we were able to reduce the code base to about 2,200 lines and make it more robust and extensible in the process. This would have taken far less time overall if the code had been refactored iteratively during the original development.
When Do You Refactor?
How do you know when refactoring is needed? Typically, you’ll notice a “code smell”—a surface indication that there is a deeper design problem. Perhaps you have a single function has grown to be hundreds of lines long, or perhaps a function has grown to accept tens of parameters. For me, the inability to quickly recognize what a given function or class is supposed to do is often a hint that a refactoring is needed. In the interest of space, I won’t go into specifics of how to refactor here, but the references listed below go into great detail. I will note, however, that having some sort of automated test(s) is very helpful for ensuring that the code still works as expected after refactoring.
In real-world research code, my general approach is to make the initial design as simple as possible and expect to refactor as I understand the problem better or as new requirements become obvious. This is based on experience that over-designed code (i.e., code that tries to anticipate future requirements or does more than is required) is particularly detrimental to understandability. Much of my research code is in Python. In Python, this approach generally means that I start by mainly writing functions. Later on, it might become obvious that certain data structures and functions should be grouped together. Or perhaps I realize that polymorphism would allow me to remove conditional statements spread throughout the code. At that time, I’ll abstract some code into classes. There are many other aspects to refactoring, but the structure of classes and functions are the ones I think about most. I’ve found that this approach prevents me from prematurely choosing the wrong abstraction, which can be far more costly than refactoring. In the course of developing a new piece of software, I’ll often do several refactorings that touch large parts of the code.
By thinking about refactoring as a natural part of the development process, you will feel more in control of your code, making future development more enjoyable. The idea with refactoring is that “you do not look at your code as some frozen construct that is not susceptible to change. Instead, you see yourself as capable of maintaining the code in optimum shape, responding efficiently to new challenges and changing the code without fear.”  Who knows, you might even come to find the refactoring process itself to be enjoyable!
- “Refactoring: improving the design of existing code” by Martin Fowler, www.refactoring.com
- “Refactoring to Patterns” by Joshua Kerievsky
- “Clean Code: A Handbook of Agile Software Craftsmanship” by Robert Cecil Martin