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Notes on Writing Discoverable Framework Code

A while back, I found myself plugged into working on a rather large .NET project. The project had been going on for a while, and I was helping out only in terms of implementation manpower; architectural decisions had long since been made. And, in fact, some of the architectural decisions had pre-dated the project altogether. There was a “framework” that these guys were using for the purpose of rapid implementations. The idea was that developers could sort of slap this framework into place and make a few tweaks to have new applications rather than having to take more time to write the code. This wasn’t especially effective, but that was the goal.

As I used (and kind of battled with) this framework, something bothered me about it that I couldn’t put my finger on at first. But as I worked with it, I started to figure it out. The framework wasn’t discoverable in the slightest and thus fought against its own goal of rapid ramp-up and development. I actually put a placeholder in my drafts folder all of those moons ago wherein I made notes about how to make a framework discoverable. I never wound up bothering to make suggestions for improvements to that framework since, luckily, it was the last time I had to use it. But I’m trying to reduce the number of unpublished drafts in my folder, so I figured I’d offer up the suggestions to the world at large so that maybe some of you reading can get some value out of them.

What Do You Mean, Discoverable?

In the last paragraph I used a word without defining it, so I’ll correct that here. To understand what I mean, ask yourself the question, “how do developers discover how to use code?” 10 years ago, the most common answer to that question would have been “read the documentation.” Ah, the bad old days. I remember doing some Linux kernel programming using a library called RTAI, and I had these binders full of documentation on the API. They described in detail what each of the hundreds of parameters for dozens of functions were and what they meant. You certainly couldn’t tell from the names of these things — they were abbreviated, cryptic, and unclear.

The API itself lent few obvious clues about how to use it, and documentation was necessary for understanding. This is an API that is not discoverable. Discoverability is the degree to which you can figure out how to use something simply by playing with and examining it. For instance, if some API had a method called “Connect_To_The_SQL_Server_Database_Using_The_Following_Connection_String(string connection_string)”, you’d be dealing with something highly discoverable; you know how to use this without doing anything but looking at the code.

Developers have a variety of strategies for discovering how to use code. Reading documentation is probably the most traditional and established method, and one with which most experienced developers are familiar. However, let’s be frank — it’s also kind of a bummer. When you’re coding, you just want to write code via experimentation, trial and error. Having to lug out a big binder and pore through it, parsing jargon-heavy text is definitely a fly in the ointment. And while I’ve seen a lot of people express the sentiment, “you have to fight through the burn or you’re not a real programmer,” the fact of the matter is that when something is drudgery, attention spans wander and productivity decreases. So while reading documentation is the most established way of discovering code, it’s also the least effective, typically.

By contrast, the most effective manner of code discovery is use. Developers start using an API in whatever way seems most intuitive to them, typically using tools like Intellisense or word completion. They have some class they’ve declared an instance of in their IDE and they type the variable name and then a period and then they see what the auto-completion tool gives them by way of options for methods. There are other ways for developers to discover APIs as well, such as reading the code when it’s available, pairing with the code’s author, discussing the code with others, etc. But by and large, discovery through experimentation and clarity of purpose is most effective.

So the important question for architects, API authors, and framework providers thus becomes “how do I make my stuff discoverable for the developers that will use it?” Here are some suggestions.

Favor composition over inheritance

Inheritance is one of the main facets of polymorphism and boy oh boy was it popular in the late 1990’s and early 2000’s, especially in Java-land. I recall working on and with inheritance hierarchies that were 10 deep before any useful functionality emerged. Inheritance was one of the best ways at the time to avoid code duplication, but we, as an industry, learned the ugly downside of this approach: confusing non-discoverability.

There is nothing quite like getting a bug report that your code isn’t working and then taking a look to see what went wrong. You look at the source control history and see no record of any change that would explain the bug. None of the classes that your class is using have changed. Is it a bug in the GUI technology or database driver or something? Nope. Turns out, someone checked in a change 3 levels above you in the inheritance hierarchy and suddenly your class behaves differently. /Sigh.

When you use composition (the practice where your class uses other classes or implements interfaces), this sort of mysterious behavior in your classes is eliminated. If your class’s behavior has changed and you haven’t changed it, you know that one of the things that you’re using must have been changed. Eliminating or minimizing inheritance gets rid of a source of confusing possible changes.

Favor few parameters over many

This is relatively straightforward, but avoid using a lot of parameters with your methods. The fewer the parameters, the fewer the amount of things that callers of your methods have to understand and remember. This is a “Golden Rule” kind of situation. When you find the method you want to use by name, but it takes 12 arguments, do you think to yourself, “perfect — just what I want!” or do you think to yourself, “ugh, why me?” Well, users of your API/framework are no different. Consolidate the parameters you require into objects if need be — this will still be easier to understand for clients of your work.

Be judicious with overload methods

Method overloads are a powerful tool, but use them sparingly. It’s nice to offer your clients options, but it’s easy to offer them too many options. Applying the “golden rule” concept from the previous section, imagine if your code autocomplete tool shows you the method that you want, but there are 20 different parameter lists. Ugh. Which one to use? This isn’t discoverable anymore because you’re going to start googling or reading documentation or something. If there’s only one option, you’re simply going to use it and move on.

Favor early binding over late binding

One of the nastiest facets of the framework that I mentioned in the introductory section of the post was rampant late binding. Late binding is a scheme where relationships are resolved at run time rather than compile time. A common example of late binding would be a scheme that uses reflection to resolve names of fields in code to strings and to sync them with values on a form. So, if you have a Customer object with a “Name” property, you use reflection to get the string “Name” and look for a matching string on the form to which to write that value. Contrast this with a scheme where you simply have a strongly typed form object and you assign a value to its “Name” property. In the former scheme, mis-typing the property name results in a confusing runtime exception whereas in the latter scenario, the code won’t compile.

Programmers understand non-compiling. It’s one of the first feedback schemes you learn with a new language and you’ll very quickly understand why the compiler complains at you. So when something doesn’t compile, it’s quick and easy to figure this out and fix it. But if something fails weirdly at runtime as with a late binding scheme, it becomes hard to figure out what’s wrong. And, more importantly, it’s very frustrating. Part of discoverability is that it’s easy to understand why the right things work and the wrong things don’t. Late binding makes this understanding far more difficult for people working with the code.

Fail as early as possible

Very closely related is the concept of failing fast and early. An important concept for programmers is a tight feedback loop — a minimum of time between when you take an action and see the result of that action. A framework that creates negligible lapses between action and reaction is a discoverable one because users can quickly understand what works and what doesn’t. If there’s a lag between action and reaction, things are harder to figure out. This is a big problem with late binding, but it would also apply to a framework that’s slow running or slow to build as well.

Make screwing up impossible

Perhaps most important to discoverability is to make screwing up as hard as possible. Have clients of your code “fall into the pit of success.” This means taking whatever steps are possible to disallow doing the wrong thing. If a class Car in your framework requires an Engine in order to work, force people using your framework to pass Engine to Car’s constructor so that if they don’t, the code won’t compile. If they pass null, throw an exception. Contrast this with a behavior where the code makes passing in an engine optional and simply limps along and maybe throws a null reference exception somewhere if it’s missing. The latter approach is confusing — you’re communicating through your API that the Engine isn’t needed, and yet you’re failing everywhere it’s not present. You’re creating a minefield for your users rather than a pit of success, and consequently making your stuff really non-discoverable.

By the way, if you liked this post and you're new here, check out this page as a good place to start for more content that you might enjoy.

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Intro to Unit Testing 9: Tips and Tricks

This, I’ve decided, is going to be the penultimate post in this series. I had originally targeted a 9 part series, finishing up with a “tips and tricks” post, the way you often see with technical books. But I think I’m going to add a 10th post to the series where I make the business case for unit testing. That seems like a more compelling wrap up to the series. So, stay tuned for one more after this.

This post is going to be a collection of tips and tricks for new and veteran unit testers.

Structure the test names for readability beyond just descriptive names.

The first step in making your unit tests true representation of business statements is to give them nice, descriptive names. Some people do this with “given, when, then” verbiage and others are simply verbose and descriptive. I like mine to read like conversational statements, and this creates almost comically long names like “GetCustomerAddress_Returns_Null_When_Customer_Is_Partially_Registered.” I’ve been given flack for things like this during my career and you might get some as well, but, do you know what? No one is ever going to ask you what this test is checking. And if it fails, no one is ever going to say “I wonder why the unit test suite is failing.” They’re going to know that it’s failing because GetCustomerAddress is returning something other than null for a partially registered customer.

When you’re writing unit tests, it’s easy to gloss over names of the tests the way you might with methods in your production code. And, while I would never advocate glossing over naming anywhere, you especially don’t want to do it with unit tests because unit test names are going to wind up in a report generated by your build machine or your IDE where as production methods won’t unless you’re using a static analysis tool with reporting, like NDepend.

But it goes beyond simply giving tests descriptive names. Come up with a good scheme for having your tests be as readable as possible in these reports. This is a scheme from Phil Haack that makes a lot of sense.
I adopted a variant of it after reading the post, and have been using it to eliminate duplication in the names of my unit tests. This consideration of where the test names will be read, how, and by whom is important. I’m not being more specific here simply because how you do this exactly will depend on your language, IDE, testing framework, build technology etc. But the message is the same regardless: make sure that you name your tests in such a way to maximize the value for those who are reading reports of the test names and results.

Create an instance field or property called “Target”

This one took a while to grow on me, but it eventually did and it did big time. Take a look at the code below, originally from a series I did on TDD:

[TestClass]
public class BowlingTest
{
	[TestClass]
	public class Constructor
	{

		[TestMethod, Owner("ebd"), TestCategory("Proven"), TestCategory("Unit")]
		public void Initializes_Score_To_Zero()
		{
			var scoreCalculator = new BowlingScoreCalculator();

			Assert.AreEqual(0, scoreCalculator.Score);
		}
	}

	[TestClass]
	public class BowlFrame
	{
		private static BowlingScoreCalculator Target { get; set; }

		[TestInitialize()]
		public void BeforeEachTest()
		{
			Target = new BowlingScoreCalculator();
		}

		[TestMethod, Owner("ebd"), TestCategory("Proven"), TestCategory("Unit")]
		public void With_Throws_0_And_1_Results_In_Score_1()
		{
			var frame = new Frame(0, 1);
			Target.BowlFrame(frame);

			Assert.AreEqual(frame.FirstThrow + frame.SecondThrow, Target.Score);
		}

		[TestMethod, Owner("ebd"), TestCategory("Proven"), TestCategory("Unit")]
		public void With_Throws_2_And_3_Results_In_Score_5()
		{
			var frame = new Frame(2, 3);
			Target.BowlFrame(frame);

			Assert.AreEqual(frame.FirstThrow + frame.SecondThrow, Target.Score);
		}

		[TestMethod, Owner("ebd"), TestCategory("Proven"), TestCategory("Unit")]
		public void Sets_Score_To_2_After_2_Frames_With_Score_Of_1_Each()
		{
			var frame = new Frame(1, 0);
			Target.BowlFrame(frame);
			Target.BowlFrame(frame);

			Assert.AreEqual(frame.Total + frame.Total, Target.Score);
		}

		[TestMethod, Owner("ebd"), TestCategory("Proven"), TestCategory("Unit")]
		public void Sets_Score_To_Twenty_After_Spare_Then_Five_Then_Zero()
		{
			var firstFrame = new Frame(9, 1);
			var secondFrame = new Frame(5, 0);

			Target.BowlFrame(firstFrame);
			Target.BowlFrame(secondFrame);

			Assert.AreEqual(20, Target.Score);
		}

		[TestMethod, Owner("ebd"), TestCategory("Proven"), TestCategory("Unit")]
		public void Sets_Score_To_25_After_Strike_Then_Five_Five()
		{
			var firstFrame = new Frame(10, 0);
			var secondFrame = new Frame(6, 4);

			Target.BowlFrame(firstFrame);
			Target.BowlFrame(secondFrame);

			Assert.AreEqual(30, Target.Score);
		}
	}

	public class BowlingScoreCalculator
	{
		private readonly Frame[] _frames = new Frame[10];

		private int _currentFrame;

		private Frame LastFrame { get { return _frames[_currentFrame - 1]; } }

		public int Score { get; private set; }

		public void BowlFrame(Frame frame)
		{
			AddMarkBonuses(frame);

			Score += frame.Total;
			_frames[_currentFrame++] = frame;
		}

		private void AddMarkBonuses(Frame frame)
		{
			if (WasLastFrameAStrike()) Score += frame.Total;
			else if (WasLastFrameASpare()) Score += frame.FirstThrow;
		}

		private bool WasLastFrameAStrike()
		{
			return _currentFrame > 0 && LastFrame.IsStrike;
		}
		private bool WasLastFrameASpare()
		{
			return _currentFrame > 0 && LastFrame.IsSpare;
		}
	}
}

If you look at the nested test class corresponding to the BowlFrame method, you’ll notice that I have a class level property called Target and that I have a method called “BeforeEachTest” that runs at the start of each test and instantiates Target. I used to be more of a purist in wanting all unit test methods to be completely and utterly self-contained, but after a while, I couldn’t deny the readability of this approach.

Using “Target” cuts out at least one line of pointless (and repetitive) instantiation inside each test and it also unifies the naming of the thing you’re testing. In other words, throughout the entire test class, interaction with the class under test is extremely obvious. Another ancillary benefit to this approach is that if you need to change the instantiation logic by, say, adding a constructor parameter, you do it one place only and you don’t have to go limping all over your test class, doing it everywhere.

I highly recommend that you consider adopting this convention for your tests.

Use the test initialize (and tear-down) for intuitive naming and semantics.

Along these same lines, I recommend giving some consideration to test initialization and tear-down, if necessary. I name these methods BeforeEachTest and AfterEachTest for the sake of clarity. In the previous section, I talked about this for instantiating target, but this is also a good place to instantiate other common dependencies such as mock objects or to build friendly instances that you pass to constructors and methods.

This approach also creates a unified and symmetric feel in your test classes and that kind of predictability tends to be invaluable. People often debug production code, but are far more likely to initially contemplate unit tests by inspecting them, so predictability here is as important as it is anywhere.

Keep Your Mind on AAA

AAA. “Arrange, Act, Assert.” Think of your unit tests in these terms at all times and you’ll do well (I once referred to this as “setup, poke, verify.“). The basic anatomy of a unit test is that you setup the world that you’re testing to exist in some situation that matters to you, then you do something, then you verify that what you did produced the result you expect. A real world equivalent might be that you put a metal rod in the freezer for 2 hours (arrange), take it out and stick your tongue on it (act), and verify that you can’t remove your tongue from it (assert).

If you don’t think of your tests this way, they tend to meander a lot. You’ll do things like run through lists of arguments checking for exceptions or calling a rambling series of methods to make sure “nothing bad happens.” This is the unit test equivalent of babbling, and you don’t want to do that. Each test should have some specific, detailed arrangement, some easily describable action, and some clear assertion.

Keep your instantiation logic in one place

In a previous section, I suggested using the test runner’s initialize method to do this, but the important thing is that you do it, somehow. I have lived the pain of having to do find and replace or other, more manual corrections when modifying constructors for classes that I was instantiating in every unit test for dozens or even hundreds of tests.

Your unit test code is no different than production code in that duplication is your enemy. If you’re instantiating your class under test again and again and again, you’re going to suffer when you need to change the instantiation logic or else you’re going to avoid changing it to avoid suffering, and altering your design to make copy and paste programming less painful is like treating an infected cut by drinking alcohol until you black out and forget about your infected cut.

Don’t be exhaustive (you don’t need to test all inputs — just interesting ones)

One thing I’ve seen occasionally with people new to unit testing and especially getting their heads around TDD is a desire to start exhaustively unit testing for all inputs. For instance, let’s say you’re implementing a prime number finder as I described in my Pluralsight course on NCrunch and continuous testing. At what point have you written enough tests for prime finder? Is it when you’ve tested all inputs, 1 through 10? 1 through 100? All 32 bit integers?

I strongly advise against the desire to do any of these things or even to write some test that iterates through a series of values in a loop testing for them. Instead, write as many tests as you need to tease out the algorithm if you’re following TDD and, in the end, have as many tests as you need to cover interesting cases that you can think of. For me, off the top (TDD notwithstanding), I might pick a handful of primes to test and a handful of composite numbers. So, maybe one small prime and composite and one large one of each that I looked up on the internet somewhere. There are other interesting values as well, such as negative numbers, one, and zero. I’d make sure it behaved correctly for each of these cases and then move on.

It might take some practice to fight the feeling that this is insufficient coverage, but you have to think less in terms of the set of all possible inputs and more in terms of the set of paths through your code. Test out corner cases, oddball conditions, potential “off by one” situations, and maybe one or two standard sorts of inputs. And remember, if later some kind of bug or deficiency is discovered, you can always add more test cases to your test suite. Your test suite is an asset, but it’s also code that must be maintained. Don’t overdo it — test as much as necessary to make your intentions clear and guard against regressions, but not more than is needed.

Use a continuous testing tool like NCrunch

If you want to see just how powerful a continuous testing tool is, check out that Pluralsight video I did. Continuous testing is game changer. If you aren’t familiar with continuous testing, you can read about it at the NCrunch website. The gist of it is that you get live, real-time feedback as to whether your unit tests are passing as you type.

Let that sink in for a minute: no running a unit test runner, no executing the tests in the IDE, and not even any building of the code. As you type, from one character to the next, the unit tests execute constantly and give you instantaneous feedback as to whether you’re breaking things or not. So, if you wander into your production code and delete a line, you should expect that you’ll suddenly see red on your screen because you’re breaking things (assuming you don’t have useless lines of code).

I cannot overstate how much this will improve your efficiency. You will never go back once you get used to this.

Unit Tests Instead of the Console

Use unit tests instead of the console or whatever else you might use to do experimentation (get comfortable with the format of the tests). Most developers have some quick way of doing experiments — scratchpads, if you will. If you make yours a unit test project, you’ll get used to having unit tests as your primary feedback mechanism.

In the simplest sense, this is practice with the unit test paradigm, and that never hurts. In a more philosophical sense, you’re starting to think of your code as a series of entry points that you can use for inspection and determining how things interact. And that’s the real, longer term value — an understanding that good design involves seams in the code and unit tests let you access those seems.

Get familiar with all of the keyboard shortcuts

Again, this is going to vary based on your environment, but make sure to learn whatever keyboard shortcuts and things your IDE offers to speed up test management and execution. The faster you are with the tests, the more frequently you’ll execute them, the more you’ll rely on them, and the more you’ll practice with them.

Your unit test suite should be a handy, well-worn tool and a comfortable safety blanket. It should feel right and be convenient and accessible. So anything you can do to wear it in, so to speak, will expedite this feeling. Take the time to learn these shortcuts and practice them — you won’t regret it. Even if you have a continuous testing tool, you can benefit from learning the most efficient way to use it. Improvement is always possible.

General Advice

Cliche as it may sound and be, the best tip I can give you overall is to practice, practice, practice. Testing will be annoying and awkward at first, but it will become increasingly second nature as you practice. I know that it can be hard to get into or easy to leave by the wayside when the chips are down and you’re starting at a deadline, but the practice will mitigate both considerations. You’ll grow less frustrated and watch the barriers to entry get lower and, as you get good, you won’t feel any inclination to stop unit testing when the stakes are high. In fact, with enough practice, that’s when you’ll feel it’s most important. You will get there with enough effort — I promise.

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Lessons in Good Naming through Absurdity

There’s something I’ve found myself stressing a lot lately to people on my team. It’s a thing that probably seems to most like nitpicking, but I think is one of the most, if not the most, under-stressed aspects of programming. I’m talking about picking good names for things.

I’ve seen that people often give methods, variables and classes abbreviated names. This has roots in times where saving characters actually mattered. Methods in C had names like strcat (concatenate a string), and that seems to have carried forward to modern languages with modern resource situations. The reader of the method is left to try to piece together what the abbreviation means, like the recipient of a text message from someone who thinks that teenager text-speak is cute.

There are other naming issues that occur as well. Ambiguity is a common one, where methods have names like “OnClick” or even “DoStuff.” You’ll also have methods that are occasionally misleading — a method called “ReadRecords” that reads in some records and then actually updates them as well. Giving this a simple name like “ReadAndUpdateRecords” would take care of this, but people don’t do it. There are other examples as well.

All of this probably seems like nitpicking, as I said a moment ago, but I contend that it isn’t. Code is read way, way more often than it is written/modified, and it’s usually read by people who don’t really understand what was going through the coder’s mind at the time of writing. (This can even include the original coder, revisiting his own code weeks or months later.) Anything that furthers understanding becomes important for saving minutes or even hours spent trying to understand. Methods with names that accurately advertise what they do save the maintenance programmer from needing to examine the implementation to see how it works. When there is a standard like this through the entirety of the code base, the amount of time saved by not having to study implementations is huge.

Toward the end of achieving this goal, one idea I had was a “naming” audit. This activity would consist of the team assembling for an hour or so, perhaps over pizza one lunch or evening, and going through the code base looking at all of the names of methods, variables, and classes. Any names that weren’t accurate or sufficiently descriptive would be changed by the group to something that was clear to all. I think the ROI on this approach would be surprisingly high.

But if you can’t do that, maybe a more distributed approach would work — one that combines the best elements of shaming and good-natured ribbing, like having the person who breaks the build buy lunch. So maybe any time you encounter a poorly named method in the code, you rename it to something ridiculous to underscore the naming problem that preceded it. You bring this to the method author’s attention and demand a better name. Imagine seeing your methods renamed to things like:

  • ShockAndAwe()
  • Blammo(int x)
  • Beat(int deadHorse)
  • Onoes(string z)
  • UseTheForceLuke()

I’m not entirely sure if I’m serious about this or not, but it would make for an interesting experiment. Absurd names kind of underscores the “dude, what is this supposed to mean” point that I’m making, and it’s a strategy other than endless nagging, which isn’t really my style. I’m not sure if I’ll try this out, but please feel free to do so yourself and, if you do, let me know how it goes.

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Static Analysis: Why You Should Care

I don’t want to go into a ton of detail on this just yet, but in broad terms, my next Pluralsight course covers the subject of static analysis. I get the sense that most people’s reaction to static analysis lies somewhere between “what’s that?” and “oh yeah, we use FX Cop sometimes.” To be sure, it’s not everyone’s reaction, but I’d say the majority falls into this category. And frankly, I think that’s a shame.

To bring things into perspective a bit, what would you do if you wanted to know how many public static methods were in a given namespace or project? I’m guessing that you’d probably hit “ctrl-shift-f” or whatever “find all in files” happens to be in your IDE, and then you’d start counting up the results, excluding spurious matches for public static classes and properties. Maybe you’d find some way to dump the results to Excel and filter or something a little more clever, but it’s still kludgy.

And what if you wanted to answer a question like “how many 20+ line methods are there in my code base?” My guess is that you basically wouldn’t do that at all. Perhaps you have an IDE plugin that offers some static analysis and LOC is a common one, but absent that, you’d probably just take a guess. And what if you wanted to know how many such methods in your code base also took a dependency on three or more framework classes? You’d probably just live with not knowing.

And living with not knowing leads to talking about code in vague generalities where loudness tends to make right. You might describe the whole reporting module as “tricky” or “crappy” or “buggy,” but what do those things really mean, aside from conveying that you more or less don’t trust that code? But what if you could run some qualitative and quantitative analysis on it and say things like “more than 80% of the methods in that module depend on that flaky third party library” or “there are several classes in there that are used by at least 40 other classes, making them extremely risky to change.” Now you have tangible, quantifiable problems for which you can find measurable solutions that can be validated. And that ability is solid gold in a profession often dominated by so-called religious arguments.

CodeReview

Static analysis of the variety that gives you detailed information about your code and warns you about potential problems combines two incredibly useful software development techniques: code review and fast feedback. Code reviews involve peer inspection of code, but it is conceptually possible to get a lot of the benefit of this activity by having the reviewers codify and store common rulesets that they would apply when doing actual reviews: no methods longer than X lines, no more code added to class Y, etc. Done this way, fast feedback becomes possible because the reviewee doesn’t actually need to find time with reviewers but can instead keep running the analysis on the code as he writes it until he gets it right.

There are plenty more benefits that I could list here. I even could talk about how static code analysis is just flat out fascinating (though that’s something of an editorial opinion). But, for my money, it makes the discussion of code quality scientific, and it dramatically speeds up the review/quality feedback loop. I think pretty much any software group could stand to have a bit of that magic dust sprinkled on it.

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Intro to Unit Testing 7: Overcoming Inertia and Objections

In this series so far, I’ve introduced the concept of unit testing, talked about how to avoid early failures and frustrations, and then dived more in depth into the subject. But now that you’re familiar with the topic and reasonably well versed in it (particularly if you’ve been practicing over the course of this series), I’d like to use that familiarity to discuss why unit testing makes sense in terms that you now better understand. And beyond that, I’d like to discuss how you can use this sense to overcome inertia and even objections that others may have.

The Case for Unit Tests

Alright, so this is the part where I offer you a laundry list, right? This is where I say that unit tests improve code quality, document your code, exercise your API, promote (or at least correlate with) good design, help you break problems into manageable chunks, expose problems earlier when finding them is cheaper, and probably some other things that I’m forgetting right now. And I believe that all of these things are true. But after a number of years of faithfully writing unit tests and practicing test-driven development (TDD), I think that I can offer those as appetizers or footnotes in the face of the real benefit: they allow you to refactor your code without fear or hesitation.

First to Contend With: Inertia

Inertia could probably be categorized as an objection, but I’m going to treat it separately since it manifests in a different way. Objections to doing something are essentially counterarguments to it. They may be lame counterarguments or excellent counterarguments, but either way, they take an active position. Inertia doesn’t. It’s either ambivalence or passive-aggressive resistance. To illustrate the difference, consider the following exchanges:

Alice: We should start trying to get our code under test.
Bob: Unit testing is stupid and a complete waste of time.

versus

Alice: We should start trying to get our code under test.
Bob: Yeah, that’d be nice. We should do that at some point.

The former is a strident (and obtuse) counterargument while the latter is an example of inactivity by inertia. In the second exchange, Bob either thinks unit testing is a good idea — but just not now — or he’s blowing sunshine at Alice while subtly saying, “not now,” so that she’ll leave him alone and stop stumping for change (i.e. the passive-aggressive approach).

In either case, the best way to overcome inertia is to counteract it with activity of your own. Inertia is the byproduct of the developer (and human in general) tendency to get stuck in a rut of doing the comfortable and familiar, so overcoming it within your group is usually just a matter of creating a new rut for them. This isn’t necessarily an easy thing to do. You’ll have to write the tests, make sure they stay up to date, demonstrate the benefits to anyone who will listen, and probably spend some time teaching others how to do it. But if you persevere and your only obstacle is inertia, sooner or later test writing will become the new normal and you’ll get there.

Red Herrings and Stupid Objections

RedHerring

Around a year ago, I blogged about a guy who made a silly claim that he wrote a lot of unit tests but didn’t check them in. The reasoning behind this, as detailed in the post, was completely fatuous. But that’s to be expected since the purpose of this claim wasn’t to explain a calculated approach but rather to cover a lack of knowledge — the knowledge of how to write unit tests.

This sort of posturing is the province of threatened Expert Beginners. It’s the kind of thing that happens when the guy in charge understands that unit testing is widely considered to be table stakes for software development professionalism but has no idea how it works. As such, he believes that he has to come up with a rationale for why he’s never bothered to learn how to do it, but he’s handicapped in inventing an explanation that makes sense by virtue of the fact that he has no idea what he’s talking about. This results in statements like the following:

  • Unit tests prevent you from adapting to new requirements.
  • Testing takes too much time.
  • It wouldn’t work with our style of writing code.
  • You’re not going to catch every bug, so why bother?
  • Writing all of your tests before writing any code is dumb.
  • Management/customers wouldn’t like it and wouldn’t want to pay for it.

I obviously can’t cover all such possible statements, but use the smell test. If it sounds incredible or stupid, it probably is, and you’re likely dealing with someone who is interested in preserving his alpha status more than creating good work product. To be frank, if you’re in an environment like that, you’re probably best off practicing your craft on the sly. You can write tests and keep quiet about it or even keep them in your own personal source control (I have done both at times in my career when in this situation) to prevent people from impeding your own career development. But the best longer term strategy is to keep your eyes and ears open for other projects to work on where you have more latitude to set policies. Do an open source project in your spare time, grab an opportunity to develop a one-off tool for your group, or maybe even consider looking for a job at an organization a little more up to speed with current software development practices. You can stay and fight the good fight, but I don’t recommend it in the long run. It’ll wear you down, and you’re unlikely to win many arguments with people that don’t let a lack of knowledge stop them from claiming expertise on a subject.

I Don’t Know How to Unit Test

With inertia and silliness set aside, let’s move on to legitimate objections to the practice. At first blush, you may be inclined to scoff at the objection, “I don’t understand it,” particularly in an industry often dominated by people unwilling to concede that their knowledge is lacking in the slightest bit in any way whatsoever. But don’t scoff — this is a perfectly reasonable objection. It’s hard and often unwise simply to start doing something with professional stakes when you don’t know what you’re doing.

If the people around you admit to not knowing how to do it, this earnest assessment often indicates at least some willingness to learn. This is great news and something you can work with. Start teaching yourself how to do it so that you can help others. Watch Pluralsight videos and show them to your coworkers as well. If your group is amenable to it, you can even set aside some time to practice as a group or to bring in consultants to get you off to a good start. This is an objection that can easily be turned into an asset.

It Doesn’t Work With the Way I Code

I originally specked out this post in the series because of a comment in the very first post, and this section is the one that addresses that comment. Arguments of this form are ones I’ve heard quite frequently over the years, and the particulars of the coding style in question vary, but the common thread is the idea that unit testing creates friction with an established routine. This isn’t the same as “I don’t know how to unit test” because the people who say this generally do know how to write tests — they must or else they wouldn’t know anything about the subject and would make up Expert-Beginner-style stupid excuses. It also isn’t the same as the inertia objection because they’re saying, “I was willing to try, but I find that this impedes me,” rather than, “meh, I dunno, I like my routine.”

My short answer to someone who has this objection is, to put it bluntly, “change the way you code.” Whatever the specifics of your approach, when you’re done, you don’t wind up with a fast-executing safety net of tests that you trust — tests that document your intentions, keep your code flexible, help prevent regressions, and force your design to be easy to use and decoupled. People who code differently than you do, in that they unit test, do wind up with those things. So figure out a way to be one of those people.

On a deeper level, though, I understand this objection because it hits closer to home. I was never the type to bluster like an Expert Beginner, nor am I prone in the slightest to inertia. (I am pretty much the opposite, frequently biting off more than I can chew.) The other objections never really applied to me, but this one did both prior to starting to write tests and prior to adopting TDD as my exclusive approach to developing. You can read that latter perspective from my very early days of blogging. Years ago, I chafed at the prospect of unit testing because spending the extra time took me out of the ‘flow’ of my coding, and I balked at TDD because I thought “why would I start writing unit tests for this code when it might be refactored completely later?” In other words, neither one of these worked with my approach.

But in both cases, I relented eventually and changed the way I coded. I didn’t just one day say, “well, I guess I’ll just start writing code differently from now on.” What happened instead was that I realized that a lot of really bright people and prominent names in the industry had coding and design styles that were compatible with writing tests so it was at least worth trying things their way. It wasn’t a matter of doing something because the cool kids were doing it or resolving to change my ways. Rather, I thought to myself, “I’ll see what all the fuss is about. Then, I’ll either like it or go back to my way of doing things armed with much better arguments as to why my way is better.” So I poured myself into a different way of doing things, forced myself to keep at it even though it was slow and awkward, and, wouldn’t you know it, I eventually came to prefer it.

Convincing people in your group to follow that lead is not going to be easy, but it is doable. The best way to do it is to earn their respect and show them results. If your code is cleaner and freer of bugs and your past projects are easy to adapt and modify, people are going to notice and ask you what your secret is, and you’ll be in a position to show them. It may seem improbable to you now, but you can go from being a person quietly teaching yourself to unit test on the side to one of the key leaders in a software department relatively quickly. You just have to commit yourself to continuous improvement and moving toward proven, effective techniques. Unit testing is just one such technique, but it is a powerful and important one.