How Can Comments be Controversial?

Comments add information to your code. They don’t impact the execution of that code. So how can they be bad? I believe that more commenting is better, and that comments are a vital tool in maintaining an agile codebase, but I’ve met many experienced developers who argue with me about comments.

The Pragmatic Programmer is an excellent book. It outlines best practices that have solidified over the last thirty years or so as software development has grown as a field. The authors share these practices as a series of tips that are justified by real world anecdotes. In a section on comments the authors say

“The DRY principle tells us to keep the low-level knowledge in the code, where it belongs, and reserve the comments for other, high-level explanations. Otherwise, we’re duplicating knowledge, and every change means changing both the code and the comments. The comments will inevitably become out of date, and untrustworthy comments are worse than no comments at all”

They make the point that obsolete comments are worse than no comments at all. This is undoubtably true, but it has been bastardized by many programmers as an excuse for not commenting. I’ve heard this excuse, along with two others to justify a lack of comments in code.

  1. The code is self-commenting.
  2. Out of date comments are worse than no comments.
  3. You shouldn’t rely on comments.

In this post I want to address each of these points. I think that thorough commenting speeds up the rate of consumption of code, and that obsolete comments are a failure in maintenance, rather than in comments in general.

1. The code is self-commenting

This is the excuse I hear most often from developers who don’t comment their code. The thinking is that clear variable and method names replace the need for comments. There are situations where this is true. For very short methods with a single purpose, I see that no additional comments may be necessary.

But most methods are not very short, and most methods achieve several things that relate to one overall purpose. So in the first place, very few methods fit the bill.

This is not a valid complaint because good comments only make the code easier to read. While the code may be legible on its own, a comment that explains the purpose of a method, or the reason for
a string operation, can only make it easier to read. The author of Clean Code agrees due to the amount of time we spend reading code.

“the ratio of time spent reading vs. writing is well over 10:1. We are constantly reading old code as part of the effort to write new code. Because this ratio is so high, we want the reading of code to be easy, even if it makes the writing harder.”

Because we spend more time reading code than writing it, a responsible programmer knows that the extra comment in a method which takes very little time to write may well save another programmer many hours of learning.

2. Out of date comments are worse than no comments

To refute this argument, lets take cars as an analogy. If you buy a car, then drive it for twenty thousand miles without an oil change, it will break down. If you did that, the fault wouldn’t be in cars in general. Rather, the fault is in the lack of maintenance.

Similary, obsolete comments are not a failure of comments in general, but a failure of the programmer who updated the code without updating the comments. That other programmers have bad practices does not justify continuing that bad practice.

Obsolete comments are a broken window. If you see them, it is your job to fix them.

So while obsolete comments ARE worse than no comments, that argument is an argument against bad coding practices, not comments in general.

3. You shouldn’t rely on comments

The argument that a programmer shouldn’t rely on comments is a subtle dig at another programmer’s skill. They’re saying “a good programmer can understand this code with no comments.”

There is a grain of a valid point there. If you consider yourself a senior programmer, then you should be accustomed to wading through large volumes of bad code and parsing out the real meaning and functionality.

But even for senior programmers, comments make it easier to read and understand the code. It’s much easier to interpret a one line summary of a block of code than to parse that block line by line. It’s much easier to store that one line in your head than it is to store the whole block in your head.

The real reason why this is the worst of excuses is that not all programmers working on your code are senior programmers. Some are green graduates straight out of college. Yes, a more experienced programmer could understand the code without comments, but it will cost that graduate hours to figure out what they could have gotten in minutes with good comments.

My Commenting Principle

Comment your code so that a green programmer can understand it.

Or…

Comment your code so that your boss can understand it.

What does it take to make your code readable by a green graduate? Imagine them going through it line by line. If there are no comments, then they need to understand every function call, and every library routine. They need to understand a bit about your architecture and date model.

Now imagine that every single line is commented.

In that scenario, a green programmer can read your file by reading one comment after the next. If they need more information, they can still drill into the code and get the details, but they can understand the overview just through comments.

The closer you can get to that, the better.

Don’t listen to the arguments against thorough commenting habits. Just point the detractors here.

Why are Side Effects Bad?

Side effects are any observable change to the state of an object. Some people might qualify this by saying that side effects are implicit or unintended changes to state.

Mutator methods, or setters, which are designed to change the state of an object, should have side effects. Accessor methods, or getters, should not have side effects. Other methods should generally try to avoid changing state beyond what is necessary for the intended task.

Why are these guidelines best practices though? What makes side effects so bad? Students have asked me this question many times, and I’ve worked with many experienced programmers who don’t seem to understand why minimizing side effects is a good goal.

For example, I recently commented out a block of my peer’s code because it had detrimental side effects. The code was intended to add color highlighting to log entries to make his debugging easier. The problem with the code was that the syntax highlighting xml was leaking into our save files. He was applying the highlight to a key, then using that key both in the log and in the save file. Worse still, he wrote this code so that it only occurred on some platforms. When I was debugging a cross platform feature, I got very unpredictable behavior and ultimately traced it back to this block.

This is an example where code was intended for one purpose, but it also did something else. That something else is the side effect, and you can see how it caused problems for other developers. Since his change was undocumented and uncommented, I spent hours tracking it down. As a team, we lost significant productivity due to a side effect.

Side effects are bad because they make a code base less agile. Side effects cause bugs that are difficult to find, and lead to code that is more difficult to maintain.

Before I continue, let me be clear that all code style guidelines should be broken sometimes. For each situation, a guideline or design pattern may be better or worse, and I recognize that we are always working in shades of gray.

Generally, a block of code should be written for one purpose. If it is a method, then it should do one thing. If another thing needs to be done with an object, then that should be encapsulated in another method.

Here’s a hypothetical example that I’ve seen played out hundreds of times in my career.

A class needs to do task X. A programmer may write a method to do task X, but he accidentally includes logic that also does task Y. Later, he may see that he needs to do task Y all alone. So he writes a method to do task Y. That’s where the problem is compounded.

Later still, the definition of task Y changes. So another programmer has to rewrite task Y. He goes to the class, changes the method for task Y alone, does a few quick tests, and proceeds on his merry way.

Then mysterious bugs start occurring. QA can’t really track them down to one thing because they only occur sporadically after task Y. Finally, it takes many man-hours to remove task Y from the method for task X.

In this example, the side effect led to code duplication, which led to trouble when updating the code, which led to bugs that cost many hours to track down. The fewer side effects you introduce, the easier your code will be to maintain.

These two examples show how side effects can derail development and why they are so inimical. We all write code with side effect occasionally, but it’s our job to figure out how to do it in a way that doesn’t make the code more difficult to maintain.

Make Explosive Soundscapes with Circular Sound

I’ve just finished work on a new musical instrument for Android devices. It’s called Circular Sound, and it’s aimed at people who like noise.



Circular Sound is similar to my other recent mobile instruments in that it combines a sampler with custom digital signal processing, and a unique interface. Sounds can be loaded from a configurable directory on the device, or you can play around with the default sounds, which are from freesound.org. Then they are loaded into spheres that are arranged in a circle on the left half of the screen. The left half of the screen is the source audio mixer, while the right half is used to control effects. The effects include waveshaping, granulation, delay, and modulation.

The goal of Circular Sound is to give a simple access point into generating various types of noise that is related in some way to the source sounds provided by the user.

Download it for free on Google Play and shoot me a comment to let me know if you make something cool with it!

Read “Sound Synthesis in Java” Today

I’ve spent the last several months working on a new book about programming synthesizers in Java. The book is finally finished and available to read on my site, and available for purchase in Kindle format on Amazon.com.

Sound Synthesis in Java Cover Image

The book has two parts. The first part is 11 chapters long and introduces basic synthesis concepts like unit generators, envelopes, filters, additive synthesis, and modulation synthesis. The later chapters deal with slightly more complex issues around granular synthesis and rendering output. The second part of the book is a list of demo projects where I show readers how to build some of the features found in popular hardware synthesizers, such as an arpeggiator.

To some degree this book is a follow up to my previous book, Sonifying Processing: The Beads Tutorial, but it differs in several key ways. First, the new book is written in Java, not Processing, so it is aimed at a slightly more technical audience. But I’ve also included more introductory material, so there is material to help along newbies as well. Second, the book is more focused on building popular music synthesizers. There is a chapter about connecting up a MIDI keyboard, and the examples focus on instrumental, tonal sounds, rather than abstract sound mangling. Third, this book is focused more on synthesis techniques than on the Beads Library in general. So each chapter teaches one specific concept related to general purpose sound synthesis techniques.

Drunk Paintings

These images were not created while I was drinking. Rather, they were created using an algorithm that combines several random walks, which are also known as drunk walks.

Poppy field and sky

Smokey

Drunk_Image_02_mars_01

Two Duets Composed by Cellular Automata

In the past few days I’ve completed several programs that compose rather nice notated music using cellular automata. Yesterday I posted seven solos generated by cellular automata. Today I am following up with two duets. Like the solos, these pieces were generated using elementary cellular automata.

All of these pieces look rather naked. In the past I’ve added tempo, dynamics, and articulations to algorithmic pieces where the computer only generated pitches and durations. Lately I feel like it’s best to present the performer with exactly what was generated, and leave the rest up to the performer. So these pieces are a bit more like sketches, in the sense that the performer will fill out some of the details.

Download the score for Two Duets Composed by Cellular Automata for any instruments by Evan X. Merz.

Seven Solos Composed by Cellular Automata

I’ve collected several pieces composed using cellular automata into one package.

This collection of seven solos for any instrument was created by computer programs that simulate cellular automata. A cellular automaton is a mathematical system containing many cellular units that change over time according to a predetermined rule set. The most famous cellular automaton is Conway’s Game of Life. Cellular automata such as Conway’s Game of Life and the ones used to compose these pieces are capable of generating complex patterns from a very small set of rules. These solos were created by mapping elementary cellular automata to music data. One automaton was mapped to pitch data and a second automaton was mapped to rhythm data. A unique rule set was crafted to generate unique patterns for each piece.

Download the score for Seven Solos Composed by Cellular Automata for any instrument by Evan X. Merz.