Systems Perfomance Advice for Programmers
Koder-with-attitude Kode Vicious provides advice for systems performance including problems with debugging, documenting, and testing the system.
Save 35% off the list price* of the related book or multi-format eBook (EPUB + MOBI + PDF) with discount code ARTICLE.
* See informit.com/terms
The time has come
The Walrus said
To speak of many things
“The Walrus and the Carpenter,” Lewis Carroll
Stepping away from the kode in front of us, we come to a slightly broader set of koding koncepts. Many people fail to understand that programming and software design aren’t just about typing hundreds of lines into an editor or IDE and then pressing Run. There are concepts we must address no matter how large or small the system is that we’re working with. As any system is built there are the problems of debugging, documenting, and testing the system as well as understanding challenges to the overall system performance, and these are some of the problems we turn to in this chapter.
2.1 Ode to the Method
The good thing about science is that it’s true whether or not you believe in it.
Neil deGrasse Tyson
If computer science is truly a science, then clearly the scientific method can be applied to solve problems that present themselves in computers and their accompanying software. A topic rarely covered in computer science programs is how one might actually apply the scientific method to software engineering. If one is not solving a problem via something akin to the scientific method, then can they be said to be debugging by faith? Faith-based debugging has, thankfully, not taken off in the way agile and scrum have, but it is definitely a method I have seen applied by many people who really ought to know better. Treating software bugs as near supernatural occurrences is a frequent joke among koders, “Did you sacrifice a chicken?” being a common retort to someone saying that they simply cannot find a bug or get the printer to work. While there are koders who are so good that they can find the bug just by scanning a page of your kode, these are few and far between, and so the best programmers learn to apply what, in essence, is the scientific method to solving their problems. In the following response I lay out a very simple way to apply the method to fixing software bugs and being confident in the fix.
I just started working for a new project lead who has an extremely annoying habit. Whenever I fix a bug and check in the fix to our code repo, she asks, ”How do you know this is fixed?” or something like that, questioning every change I make to the system. It’s as if she doesn’t trust me to do my job. I always update our tests when I fix a bug, and that should be enough, don’t you think? What does she want, a formal proof of correctness?
I Know Because I Know
Dear I Know,
Working on software is more than just knowing in your gut that the code is correct. In actuality, no part of working on software should be based on gut feelings, because, after all, software is supposed be a part of computer science, and science demands proof.
One of the problems I have with the current crop of bug-tracking systems (and trust me, this is only one of the problems I have with them) is that they don’t do a good job of tracking the work you’ve done to fix a bug. Most bug trackers have many states a bug can go through: new, open, analyzed, fixed, resolved, closed, etc., but that’s only part of the story of fixing a bug, or doing anything else with a program of any size.
A program is an expression of some sort of system that you, or a team, are implementing by writing it down as code. Because it’s a system, you have to have some way of reasoning about that system. Many people will now leap up and yell, ”Type systems!” and ”Proofs!” and other things about which most working programmers have no idea and which they are not likely ever to come into contact with. There is, however, a simpler way of approaching this problem that does not depend on a fancy or esoteric programming language: use the scientific method.
When you approach a problem, you ought to do it in a way that mirrors the scientific method. You probably have an idea of what the problem is. Write that down as your theory. A theory explains some observable facts about the system. Based on your theory, you develop one or more hypotheses about the problem. A hypothesis is a testable idea for solving the problem. The nice thing about a hypothesis is that it is either true or false, which works well with our Boolean programmer brains: either/or, black or white, true or false, no ”fifty shades of gray.”
The key here is to write all of this down. When I was young, I never wrote things down because I thought I could keep them all in my head. But that was nonsense; I couldn’t keep them all in my head, and I didn’t know the ones I’d forgotten until my boss at the time asked me a question I couldn’t answer. Few things suck as much as knowing that you’ve got a dumb look on your face in response to a question about something you’re working on.
Eventually I developed a system of note taking that allowed me to make this a bit easier. When I have a theory about a problem, I create a note titled THEORY, and write down my idea. Under this, I write up all my tests (which I call TEST because, like any good programmer, I don’t want to keep typing HYPOTHESIS). The note-taking system I currently use is Org mode in Emacs, which lets you create sequences that can be tied to hot keys, allowing you to change labels quickly. For bugs, I have labels called BUG, ANALYZED, PATCHED, —, and FIXED, while for hypotheses I have either PROVEN or DISPROVEN.
I always keep both the proven and disproven hypotheses. Why do I keep both? Because that way I know what I tried and what worked and what failed. This proves to be invaluable when you have a boss with OCD, or, as they like to be called, ”detail oriented.” By keeping both your successes and failures, you can always go back, say in three months when the code breaks in a disturbingly similar way to the bug you closed, and look at what you tested last time. Maybe one of those hypotheses will prove to be useful, or maybe they’ll just remind you of the dumb things you tried, so you don’t waste time trying them again. Whatever the case, you should store them, backed up, in some version-controlled way. Mine are in my personal source-code repo. You have your own repo, right? Right?!