Things Every Programmer Should Know: $22, #23, #24

Here are the 97 Things Every Programmer Should Know project, pearls of wisdom for programmers collected from leading practitioners, published by O'Reilly (license link).


You can go to read the previous very interesting points #19, #20, #21.

22. Do Lots of Deliberate Practice by Jon Jagger


Deliberate practice is not simply performing a task. If you ask yourself "Why am I performing this task?" and your answer is "To complete the task," then you're not doing deliberate practice.
You do deliberate practice to improve your ability to perform a task. It's about skill and technique. Deliberate practice means repetition. It means performing the task with the aim of increasing your mastery of one or more aspects of the task. It means repeating the repetition. Slowly, over and over again. Until you achieve your desired level of mastery. You do deliberate practice to master the task not to complete the task.
The principal aim of paid development is to finish a product whereas the principal aim of deliberate practice is to improve your performance. They are not the same. Ask yourself, how much of your time do you spend developing someone else's product? How much developing yourself?
How much deliberate practice does it take to acquire expertise?
  • Peter Norvig writes that "It may be that 10,000 hours [...] is the magic number."
  • In Leading Lean Software Development Mary Poppendieck notes that "It takes elite performers a minimum of 10,000 hours of deliberate focused practice to become experts."
The expertise arrives gradually over time — not all at once in the 10,000th hour! Nevertheless, 10,000 hours is a lot: about 20 hours per week for 10 years. Given this level of commitment you might be worrying that you're just not expert material. You are. Greatness is largely a matter of conscious choice. Your choice. Research over the last two decades has shown the main factor in acquiring expertise is time spent doing deliberate practice. Innate ability is not the main factor.
  • Mary: "There is broad consensus among researchers of expert performance that inborn talent does not account for much more than a threshold; you have to have a minimum amount of natural ability to get started in a sport or profession. After that, the people who excel are the ones who work the hardest."
There is little point deliberately practicing something you are already an expert at. Deliberate practice means practicing something you are not good at.
  • Peter: "The key [to developing expertise] is deliberative practice: not just doing it again and again, but challenging yourself with a task that is just beyond your current ability, trying it, analyzing your performance while and after doing it, and correcting any mistakes."
  • Mary: "Deliberate practice does not mean doing what you are good at; it means challenging yourself, doing what you are not good at. So it's not necessarily fun."
Deliberate practice is about learning. About learning that changes you; learning that changes your behavior. Good luck.

23. Domain-Specific Languages by Michael Hunger

Whenever you listen to a discussion by experts in any domain, be it chess players, kindergarten teachers, or insurance agents, you'll notice that their vocabulary is quite different from everyday language. That's part of what domain-specific languages (DSLs) are about: A specific domain has a specialized vocabulary to describe the things that are particular to that domain.

In the world of software, DSLs are about executable expressions in a language specific to a domain with limited vocabulary and grammar that is readable, understandable, and — hopefully — writable by domain experts. DSLs targeted at software developers or scientists have been around for a long time. For example, the Unix 'little languages' found in configuration files and the languages created with the power of LISP macros are some of the older examples.


DSLs are commonly classified as either internal or external:

  • Internal DSLs are written in a general purpose programming language whose syntax has been bent to look much more like natural language. This is easier for languages that offer more syntactic sugar and formatting possibilities (e.g., Ruby and Scala) than it is for others that do not (e.g., Java). Most internal DSLs wrap existing APIs, libraries, or business code and provide a wrapper for less mind-bending access to the functionality. They are directly executable by just running them. Depending on the implementation and the domain, they are used to build data structures, define dependencies, run processes or tasks, communicate with other systems, or validate user input. The syntax of an internal DSL is constrained by the host language. There are many patterns — e.g., expression builder, method chaining, and annotation — that can help you to bend the host language to your DSL. If the host language doesn't require recompilation, an internal DSL can be developed quite quickly working side by side with a domain expert.

  • External DSLs are textual or graphical expressions of the language — although textual DSLs tend to be more common than graphical ones. Textual expressions can be processed by a tool chain that includes lexer, parser, model transformer, generators, and any other type of post-processing. External DSLs are mostly read into internal models which form the basis for further processing. It is helpful to define a grammar (e.g., in EBNF). A grammar provides the starting point for generating parts of the tool chain (e.g., editor, visualizer, parser generator). For simple DSLs, a handmade parser may be sufficient — using, for instance, regular expressions. Custom parsers can become unwieldy if too much is asked of them, so it makes sense to look at tools designed specifically for working with language grammars and DSLs — e.g., openArchitectureWare, ANTlr, SableCC, AndroMDA. Defining external DSLs as XML dialects is also quite common, although readability is often an issue — especially for non-technical readers.
You must always take the target audience of your DSL into account. Are they developers, managers, business customers, or end users? You have to adapt the technical level of the language, the available tools, syntax help (e.g., intellisense), early validation, visualization, and representation to the intended audience. By hiding technical details, DSLs can empower users by giving them the ability to adapt systems to their needs without requiring the help of developers. It can also speed up development because of the potential distribution of work after the initial language framework is in place. The language can be evolved gradually. There are also different migration paths for existing expressions and grammars available.







24. Don't Be Afraid to Break Things by Mike Lewis


Everyone with industry experience has undoubtedly worked on a project where the codebase was precarious at best. The system is poorly factored, and changing one thing always manages to break another unrelated feature. Whenever a module is added, the coder's goal is to change as little as possible, and hold their breath during every release. This is the software equivalent of playing Jenga with I-beams in a skyscraper, and is bound for disaster.
The reason that making changes is so nerve wracking is because the system is sick. It needs a doctor, otherwise its condition will only worsen. You already know what is wrong with your system, but you are afraid of breaking the eggs to make your omelet. A skilled surgeon knows that cuts have to be made in order to operate, but the skilled surgeon also knows that the cuts are temporary and will heal. The end result of the operation is worth the initial pain, and the patient should heal to a better state than they were in before the surgery.
Don't be afraid of your code. Who cares if something gets temporarily broken while you move things around? A paralyzing fear of change is what got your project into this state to begin with. Investing the time to refactor will pay for itself several times over the life cycle of your project. An added benefit is that your team's experience dealing with the sick system makes you all experts in knowing how it should work. Apply this knowledge rather than resent it. Working on a system you hate is not how anybody should have to spend their time.
Redefine internal interfaces, restructure modules, refactor copy–pasted code, and simplify your design by reducing dependencies. You can significantly reduce code complexity by eliminating corner cases, which often result from improperly coupled features. Slowly transition the old structure into the new one, testing along the way. Trying to accomplish a large refactor in "one big shebang" will cause enough problems to make you consider abandoning the whole effort midway through.
Be the surgeon who isn't afraid to cut out the sick parts to make room for healing. The attitude is contagious and will inspire others to start working on those cleanup projects they've been putting off. Keep a "hygiene" list of tasks that the team feels are worthwhile for the general good of the project. Convince management that even though these tasks may not produce visible results, they will reduce expenses and expedite future releases. Never stop caring about the general "health" of the code.



The 'episodes' #25, #26, #27 come in the next posting :) 

Cheers!

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