September Writing Challenge, Post 24: Three Things I Wish American Tech Culture Would Learn

Note: I’ve had a couple things holding my attention this week, and as a result missed a couple of days of the writing challenge. I’ll catch up.

One more note: I’m having a slightly rant day. Bear with me.

There are a bunch of things that could be done to make the tech culture more sane and humane. Here are three that rank highly on my list:

1. Working more hours does not necessarily make you more productive. In fact, it may make you far, far less so. We work in one of the few professions where it is possible to do negative work on a daily basis – that is, to make the code worse than we left it. We are more likely to do this when we work long hours. Unfortunately, both American work culture and the tech subculture seek to twist overwork into a virtue. It’s not. Overwork leads to bad decisions. If your boss doesn’t understand this, give him the slide deck I linked earlier in this paragraph (which contains a ton of researched information on productivity topics beyond just hours). If he willfully ignores the facts and says he doesn’t believe it, go work for someone smarter, and let him fail on someone else’s broken back. Also: If you think you’re somehow the exception to this, you’re not. There’s ample research out there – I urge you to look it up.

2. Trading sleep for work just makes you dumber, not more productive. This goes hand-in-hand with the issue of long hours; as with overwork, our culture makes a badge of honor out of sleep deprivation. (I was guilty of this myself when I was younger.) When we don’t get enough sleep, it degrades the quality of our work, and our ability to notice how much our work has degraded. This may be a reason so many people think they’re exceptional in this regard. Spoiler: They’re not. Again, there’s loads of research; Google is your friend.

3. The software profession is not a meritocracy. At least, it’s not if you’re black or a woman. This is made worse by the fact that white guys in the profession often think they’re too smart to have unconscious biases about race, gender, sexuality, &c. It’s made worse still by the fact that most of us in the profession who are any good at it actually did work hard to get there, and feel there’s merit in the rewards we’ve gathered. But if it’s not a meritocracy for everyone, it’s not a meritocracy for anyone, and those of us on the inside need to check our privilege and start examining our own behavior.


September Writing Challenge, Post 22: The Customer Is Always Right

If you read yesterday’s post, the title of today’s might seem odd.

If you are designing or writing software for someone other than yourself, you are ethically bound to give your client or employer your best advice on how to meet the project’s goals. (Being persuasive in this is one of the big reasons you should cultivate your communication skills.) Whoever is paying for your skills will then take your advice, or they will not. It could go either way, for reasons that are largely out of your hands.

Whichever way that goes, you are then ethically bound to do it the way the customer wants. Their project, their money. Sometimes, that means implementing things – often user experiences, but sometimes deeper technical details – in a way you know to be somewhere on the scale from “suboptimal” to “imbecilic”. Sometimes, deliberately delivering less than the best possible product* at someone else’s request can fall on an emotional scale from “mildly annoying” to “soul-eroding”.

If that’s too much for you, you could go and make your own product. If you don’t have the savings cushion to take that leap, you could work on a side project on your own time – that can be a real sanity-saver (and a great way to sharpen your skills). And when all else fails, learn this mantra: “Not my circus, not my monkeys.” However you do it, it can be valuable to learn to distance yourself emotionally from work that does not belong to you, when the need arises. Software and business are both complex endeavors, and their intersection will always involve compromise.

Give your customer your best advice, then build the very best version of the thing they are paying for, and know that at the end they are getting what they asked for and deserve the results, for good or ill.

And, of course, make sure that best advice you gave earlier in the project is documented somewhere, with your name attached. Couldn’t hurt.

* If you’re working on medical devices or air traffic control software, and someone could end up maimed or dead, the advice in this post is less applicable. Buck up and push your case harder, in that event.

September Writing Challenge, Post 21: The Customer Doesn’t Know Shit

If you are designing or writing software for someone other than yourself, you’ll spend some about of time wanting to roll your eyes at people thinking they know how to do your job. Non-technical product managers will suggest specific technical solutions that they’ve heard of but don’t clearly understand. (MongoDB seems to turn up a lot in this context, for no reason I can discern.) Salespeople with no special background in UX design will prescribe inappropriate or outdated UI idioms. (Hamburger menus, a.k.a. pancake menus, still retain a lot of mindshare.)

Your natural and completely appropriate reaction to this sort of thing might be to want to start hitting people with a shovel, but this is bad for repeat business, and not legal in some places.

The thing you have to remember is that these nice people hired or contracted you (or your employer) because they don’t know how to do what you do, even if they sometimes forget this during requirements definition. You’re the expert. When a client falls into buzzword glossolalia, thinking he’s offering a valid technical solution, a better approach is to holster your shovel and say something like: “Let’s not get bogged down in technical details this early. Why don’t we take a step back, and you tell me what you want to accomplish by doing that, and then I can do a proper assessment of whether X is the right tool for the job.”

Gently draw the non-technical client’s attention away from the technical questions best left to you; bring your focus and theirs to the business problem they want to solve, and about which you may reasonably hope they know something. I’ve yet to have a client object to me caring about their problems and wanting to choose the best way to solve them. (Of course, then you’re on the hook to actually solve them, but that’s a topic for another post.)

There is, of course, a flip side to this, but I’ll write about that tomorrow.

September Writing Challenge, Post 18: The D in SOLID

Note: This is the fifth of five posts I’m writing on the SOLID principles of object-oriented programming. Part 1: S, Part 2: O, Part 3: L, Part 4: I

Any discussion of the Dependency Inversion Principle should start by answering the question: What, exactly, is being inverted?

A lot of object-oriented systems start with classes mapping to the higher-level requirements or entities; these get broken down and individual capabilities get drawn out into other classes, and so on. The tendency is to wind up with a pyramidal dependency structure, where any change in the lower reaches of the pyramid tends to bubble up, touching higher and higher-level components.

As an example, let’s think about the Service/Endpoint/Marshaller classes I discussed in my earlier post on the Single Responsibility Principle. It would be very easy to start writing the service class, decide to break out the Endpoint class, and do so in a way that made assumptions that you were calling an HTTP web service – for example, you might assume that all responses from the service would have a valid HTTP response code, or that parameters had to be packaged as a URL-encoded query string.

So what happens if your requirements change such that you must directly call a remote SQL store using a different protocol? You’re going to have to change at least two classes, because of assumptions you made about the nature of your data source.

With the Dependency Inversion Principle, we are told that first, we should not write high-level code that depends on low-level concretions – we should connect our components via abstractions; and second, that these abstractions should not depend on implementation details, but vice versa. I’ve seen the “inversion” part of DIP explained a few different ways, but what I see being inverted is the naïve design’s primacy of implementation over interface.

When you start thinking about how to break down subcomponents, take a step back and think about the interfaces between components, and do your best to sanitize them – remove anything that might bite you if implementation details change.

In the case of the Endpoint, that might mean writing an interface that takes a dictionary of parameter names and values, with no special encoding, and providing for success and failure callbacks. A success callback could give you some generic string or binary representation of the data you requested (which can be passed to a parser/marshaller next). The arguments to the failure callback would be a generic error representation (most platforms have one), with an appropriate app-specific error code and message – not an HTTP status code, or anything else dependent on your data source.

DIP is a key way of limiting technical risk; in this example, after we have changed the interface to be generic with respect to the data source being called, a change to the Endpoint class requirements necessitates little or no corresponding change to the Service class, and vice versa.

The Obligatory Recap

Over these past five posts, I’ve covered five principles for building resilient object-oriented systems, with resiliency being defined as resistance to common classes errors, low cost of change, and high comprehensibility (i.e., well-managed complexity).

Here are all five once more, not with their canonical formulations (you could get that from the Wikipedia page on SOLID), but with my own distillation of the core lesson (IMHO) from each:

  • Single Responsibility Principle: Give each class one thing to do, and no more.
  • Open/Closed Principle: Extend components, rather than modifying them.
  • Liskov Substitution Principle: Stay aware of the promises your classes make, and don’t break those promises in subclasses.
  • Interface Segregation Principle: Classes should advertise capabilities discretely and generically.
  • Dependency Inversion Principle: Details should depend on abstractions, never the other way around.

Go forth, and write some awesome software.

Next, I’ll post something lighter and non-technical. Promise.