Robert Marianski

Always two there are, a master and an apprentice

When learning a trade, the master/apprentice relationship can be tremendously useful. It is a slow path, but an effective one. It’s a great way to pass on the lessons from the past.

And not only is the knowledge of skills passed on to future generations, so is the knowledge of common pitfalls. As an apprentice begins to make mistakes, the master can correct them immediately, potentially avoiding much of the headache associated with recovering from the error. And we all know that the earlier an error can be recovered from, the smaller the consequences.

However, you don’t see very much of this in the programming world. I think there are a few reasons for this.

1. Being self taught is encouraged. If you can learn yourself just by rtfm, then you haven’t wasted anybody else’s time.
2. Programmers all think that they can solve the problem better themselves.
3. There tends to be a high turnover in the programming industry.

But then, each generation is doomed to repeating the same mistakes. And with programming, it’s difficult to know when you’re making a mistake. But, a master can point this out right away, and correct it before it becomes a major problem. This can save an enormous amount of time, and greatly speed up the learning process.

Programmers, and those expecting programming based solutions, are usually impatient. There’s enormous pressure to release the next best thing yesterday. The programming field also changes rapidly, and it will probably continue to change at an even faster rate in the future. It should be no surprise then that programmers are in a rush.

But the flip side is that it takes a while to become a great programmer. We really need to experience many failures before we can identify how to architect successes. And contrary to popular belief, it takes years to become proficient.

If there are no silver bullets, then I think that encouraging master/apprentice relationships can help propel us as a whole in the right direction faster.

We’ve all had our frustrating moments with computers. We bang our heads against the walls for quite some time, and no matter what we try, the computer responds with a clever “I thought you might try that, here’s your error.” And then, we try talking to someone else about it, and they usually have a brilliant idea that solves everything elegantly. Then we’re left scratching our heads, wondering why we didn’t think of that before.

Lots of problems get solved like this simply because they are looked at from a fresh perspective. It’s easy to get lost in the details of a problem. When we keep our heads down, it’s hard to realize that we were just approaching the problem from the wrong angle.

Programming is a game of insight.

I think that sums up the essence of programming. The most significant gains are often those that shed the problem in a new light.

But not all reevaluations of a problem lead to successes. In fact, I would argue that most of them don’t. But the ones that do work, usually do so in a big way. Given that programming is this give and take process, progress often isn’t linear. There isn’t a lot of progress, or it looks like things are getting worse, and then suddenly, there’s a big jump.

This makes it especially difficult to measure, assuming it’s even possible to measure at all. Any formal attempts at measurements results in programmers optimizing for the local maxima. This ends up detracting from productivity.

But regardless of whether we can keep track of it or not, it’s important to foster an environment that encourages creativity. A single idea can change everything.

We all go through programming disasters. It’s hard, if not impossible, to always make the right decisions. And after we’ve steered the ship back on course and we’re out of the storm, we try to review if there was any way we could have prevented the storm in the first place. This reflection is crucial, and is one of the best ways we can improve ourselves.

What becomes dangerous however, is when these reviews turn into policies, or mandates. To explain why, I’ll summarize an anecdote which I’ve come across from the Extreme Programming book.

A mother was baking a ham with her daughter, and she noticed that the ends were cut off. She asked her mother why, to which the mom responded: “I don’t know. That’s the way my mother always did it. I’ll ask her.” So the mother asked the grandmother why the ends of the ham were cut off, and she said: “I don’t know. That’s the way my mother always did it. I’ll ask my mother”. And the great grandmother’s response was: “My oven was too small, so I had to cut off the ends to have it fit”.

Blindly following policies can lead to extra steps that can work against us. Rather than come up with new policies, it’s better to come up with principles that can inform future decisions. It’s more important to understand the reasons behind what those policies would have been.

On that note, dogma itself is dangerous. When we start blindly following rules, we start becoming simple machines. It tends to stifle creativity, which is the worst thing that can happen to programmers. Programming itself, after all, is a creative process.

I’ve seen a trend by programmers to be completely against any form of duplication. After all, there are extremely compelling motivations for this idea. Programmers have all copy/pasted code to get something working, with the reason being that it usually ends up working much faster in the short term. But then when all that new code needs to get updated, we’ve all forgotten to make the change to all the pieces that required it. And bingo, we have a new bug.

Then we look back on it and what’s to blame? Not that we forgot to make the change everywhere, which was just the symptom. The problem was that it was possible to change one of the parts, and forget to update the other. It should have been refactored to avoid the duplication, so that a change in one location would naturally affect all the other paths. This would have prevented the bug from even being possible.

But like most dogmas taken too far, this can get you into trouble. Here’s a very contrived example, and granted most of us don’t think this way, but I’ve experienced somewhat similar arguments for avoiding duplication where I thought it was just silly.


a = 7
b = 3 + 12
c = 18 - 2
d = 9 * 6


Try to pretend that these are real calculations, and there are not just hardcoded numbers here. Can you spot the duplication? Normally you’d say there isn’t, but I can argue that there is. You have four assignments, and 3 mathematical operations. Isn’t that duplication? Here’s the “reduced” version:


vars = ['a', 'b', 'c', 'd']
arguments = [(), (3, 12), (18, 2), (9, 6)]
ops = [lambda *x:7, operator.add, operator.sub, operator.mul]
for var, args, op in zip(vars, arguments, ops):
    globals()[var] = op(*args)


Notice how I’ve eliminated all the duplication? And wasn’t it clever of me to fit the simple assignment in the first example to a no-op? The logic is all now in a single line compared to the 4 up above. I can argue that although it’s actually more lines of code, I can effectively move all but the loop into configuration. Now people can add more variables to the global namespace, with an arbitrary operation performed on any number of arguments, all through configuration! What a wonderful and extensive system I’ve created!

The perceptive reader will have discovered that I was being a tad bit sarcastic. (My co-workers will all tell you that I’m subtle). So which is easier to understand? Which would you rather maintain? Readers with no python experience will probably understand the first code snippet. But you probably need to know python to even attempt to understand what’s going on in the second.

So if you’re going to follow dogmas, policies, or rules, then follow this one:

Always use your brain.

We all have different ideas on when we should clean things up. It could be a dirty room, cluttered desk, messy closet, or just too much stuff lying around. We also prefer to keep things a certain way, which can be very individualistic. From the outside a desk with tons of paper lying around could look like it could use some rearranging, but the desk’s owner might be able to find anything at a moment’s notice.

Given how different we all are with tolerating visual clutter, it’s not too surprising that there’s a wide range of opinions on when we should clean up our code. Many books have been written completely focused on this very issue, and in the end, I think it’s still much more of an art than a science. In fact, I believe programming itself is much more of an art than a science, but that’s the topic for a whole other discussion.

Then there’s a time for spring cleaning too. And we’re always surprised when we find some really dirty stuff in the nooks and crannys. But in the real world, we usually do end up cleaning it up. (That is, if you’re not as lazy as I am). In the virtual world however, some cleanup tasks are truly daunting, and it may be easier to leave the dirty things the way they are. After all, it might not look pretty, but it works.

And similar to how the owner of a cluttered desk can find something for us quickly, old dirty code tends to work predictably as well. But if we change it a bit, we can no longer guarantee that it’ll work the same way. Unit tests can help here, but we still can’t make any guarantees. And when you move an old couch from one corner of the room to the other, you realize that there was a whole lot more dust under there than you thought. That dust also has to get cleaned up. And after some time cleaning up, we ask ourselves, was it really worth it? That couch really wasn’t all that bad where it was.

But, the advantages gained by cleaning up can outweigh its costs and risks. After all, if something becomes simpler and easier to understand, we stand to gain every single time someone works with it. Add up the time for all these future interactions, and the refactoring can more than pay for itself.

However, like weatherman and traders already know, it’s hard to predict the future. (They’ll never admit it though. What I think these guys really excel at is coming up with excuses. ) But, it’s very easy to predict the past. And although we may pay a small price each time we end up working around the dirt, we start learning where all the dirty areas are. This puts us in a better position to clean up more effectively in the future.

Refactoring usually alters the abstractions used. A new layer could get added that simplifies complex interactions by handling those details. Or extra layers that get in the way are removed to produce simpler code. But if a new problem comes along that doesn’t fit into these new abstractions, then the game is up. Chances are we’ll put in a hack to work around that “edge case”, and those can start to pile up, especially if other programmers start putting their hands in the mix. And before you know it, we’ll be talking about a new refactoring.

I’m not arguing that we should never clean up code though. I’m just pointing out that there are often more factors involved when thinking about cleaning up than just making the code prettier. What cleaning up does seem to do is improve morale. Most programmers would much rather write a 1000 new lines of clean, sparkling code, rather than try to figure out the ten lines in a 10000 line messy codebase that need to be modified. And when bold, noble undertakings like these are launched, I start to feel like Braveheart just gave me a speech before an impossible battle.

And what usually ends up happening is that the small splinter cell team beats the odds. But why? There’s too much work and not enough time. Well, I think that the answer is simple: the programmers work harder. And the reason for that is that they are a whole lot more motivated with the prospect of a fresh start instead of trying to keep a big ball of mud together.

In the end, major refactorings are a tough call either way. And the technical issues might not be the whole story. Social issues can also play a role in the decision too. Programmers tend to take attacks on their code personally, and arguments can turn into personal vendettas quickly.

But just like we’re always able to find ways around a messy desk, we find ways around our technical problems too. And regardless of the decision, thinking about the problem gives us more insight into it. So ultimately, we’re better off anyway after the exploration step. To use a cliche:

The journey is more important than the destination.

When faced with a new programming challenge, it can usually be approached from one of two ways: the front end or the back end. Each has its merits, and completely focusing on one or the other is a recipe for disaster. But, I think that most programmers tend to be a little “backend heavy”. I’d almost go so far as to say that it’s difficult to call yourself a programmer without being biased this way. After all, programmers enjoy solving complex problems by creating new powerful abstractions. If not, you’d be a little nuts to go through all the frustrations of programming without enjoying watching something work.

What I find surprising is that if a problem is too simple, it’s the programmers themselves are the ones that create more complexity. I find myself doing this all the time. “Well what if the user wanted to do foo and not bar. Instead of hard coding this action, I can add a new <insert cool pattern/framework/abstraction here> and the application will then support any action. Then we can make a user preference and new configuration management system that users can tailor to their needs”. This cascades of course, and before you know it, you’ve created a new framework. It’s just so easy to get lost in it, that programmers sometimes forget to ask the obvious question: “hold on a sec, why are we doing this again? Let’s just cross that bridge when we get to it”.

Focusing completely on the front end can be just as problematic though. Security holes, performance bottlenecks, and general lack of flexibility can hold you back too, perhaps even more so. Any one of these issues can be devastating. Then it’ll leave you wishing you had spent just a whee bit more on prevention, instead of having to pay so much for the cure.

Right about now it sounds like my point is that everybody should just do things right from the beginning, without wasting time building anything unnecessary. But realistically, I think the most important thing is to “use your brain”. The applications that programmers build are supposed to be helping users in some way. Refactoring is supposed to help programmers maintain their code. Writing tests is supposed to help maintain confidence in the code base. User testing is supposed to help inform usability improvements to the application itself. If something isn’t working like it should, maybe it needs to change, or be rethought. It’s always healthy to take a step back and review how things are going. I think Einstein said it best:

Everything should be made as simple as possible, but not one bit simpler.