Refactoring and Design
Refactoring has a special role as a complement to design. When I first learned to program, I just wrote the program and muddled my way through it. In time I learned that thinking about the design in advance helped me avoid costly rework. In time I got more into this style of upfront design. Many people consider design to be the key piece and programming just mechanics. The analogy is design is an engineering drawing and code is the construction work. But software is very different from physical machines. It is much more malleable, and it is all about thinking. As Alistair Cockburn puts it, “With design I can think very fast, but my thinking is full of little holes.”
One argument is that refactoring can be an alternative to upfront design. In this scenario you don’t do any design at all. You just code the first approach that comes into your head, get it working, and then refactor it into shape. Actually, this approach can work. I’ve seen people do this and come out with a very well-designed piece of software. Those who support Extreme Programming often are portrayed as advocating this approach.
Although doing only refactoring does work, it is not the most efficient way to work. Even the extreme programmers do some design first. They will try out various ideas with CRC cards or the like until they have a plausible first solution. Only after generating a plausible first shot will they code and then refactor. The point is that refactoring changes the role of upfront design. If you don’t refactor, there is a lot of pressure in getting that upfront design right. The sense is that any changes to the design later are going to be expensive. Thus you put more time and effort into the upfront design to avoid the need for such changes.
With refactoring the emphasis changes. You still do upfront design, but now you don’t try to find the solution. Instead all you want is a reasonable solution. You know that as you build the solution, as you understand more about the problem, you realize that the best solution is different from the one you originally came up with. With refactoring this is not a problem, for it no longer is expensive to make the changes.
An important result of this change in emphasis is a greater movement toward simplicity of design. Before I used refactoring, I always looked for flexible solutions. With any requirement I would wonder how that requirement would change during the life of the system. Because design changes were expensive, I would look to build a design that would stand up to the changes I could foresee. The problem with building a flexible solution is that flexibility costs.
FlexibleFlexible solutions are more complex than simple ones. The resulting software is more difficult to maintain in general, although it is easier to flex in the direction I had in mind. Even there, however, you have to understand how to flex the design. For one or two aspects this is no big deal, but changes occur throughout the system. Building flexibility in all these places makes the overall system a lot more complex and expensive to maintain. The big frustration, of course, is that all this flexibility is not needed. Some of it is, but it’s impossible to predict which pieces those are. To gain flexibility, you are forced to put in a lot more flexibility than you actually need.
With refactoring you approach the risks of change differently. You still think about potential changes, you still consider flexible solutions. But instead of implementing these flexible solutions, you ask yourself, “How difficult is it going to be to refactor a simple solution into the flexible solution?” If, as happens most of the time, the answer is “pretty easy,” then you just implement the simple solution.
Refactoring can lead to simpler designs without sacrificing flexibility. This makes the design process easier and less stressful. Once you have a broad sense of things that refactor easily, you don’t even think of the flexible solutions. You have the confidence to refactor if the time comes. You build the simplest thing that can possibly work. As for the flexible, complex design, most of the time you aren’t going to need it.
It Takes Awhile to Create Nothing
The Chrysler Comprehensive Compensation pay process was running too slowly. Although we were still in development, it began to bother us, because it was slowing down the tests.
Kent Beck, Martin Fowler, and I decided we’d fix it up. While I waited for us to get together, I was speculating, on the basis of my extensive knowledge of the system, about what was probably slowing it down. I thought of several possibilities and chatted with folks about the changes that were probably necessary. We came up with some really good ideas about what would make the system go faster.
Then we measured performance using Kent’s profiler. None of the possibilities I had thought of had anything to do with the problem. Instead, we found that the system was spending half its time creating instances of date. Even more interesting was that all the instances had the same couple of values.
When we looked at the date-creation logic, we saw some opportunities for optimizing how these dates were created. They were all going through a string conversion even though no external inputs were involved. The code was just using string conversion for convenience of typing. Maybe we could optimize that.
Then we looked at how these dates were being used. It turned out that the huge bulk of them were all creating instances of date range, an object with a from date and a to date. Looking around little more, we realized that most of these date ranges were empty!
As we worked with date range, we used the convention that any date range that ended before it started was empty. It’s a good convention and fits in well with how the class works. Soon after we started using this convention, we realized that just creating a date range that starts after it ends wasn’t clear code, so we extracted that behavior into a factory method for empty date ranges.
We had made that change to make the code clearer, but we received an unexpected payoff. We created a constant empty date range and adjusted the factory method to return that object instead of creating it every time. That change doubled the speed of the system, enough for the tests to be bearable. It took us about five minutes.
I had speculated with various members of the team (Kent and Martin deny participating in the speculation) on what was likely wrong with code we knew very well. We had even sketched some designs for improvements without first measuring what was going on.
We were completely wrong. Aside from having a really interesting conversation, we were doing no good at all.
The lesson is: Even if you know exactly what is going on in your system, measure performance, don’t speculate. You’ll learn something, and nine times out of ten, it won’t be that you were right!
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