Stories about Software


Introduction to C# Lambda Expressions

Today, I’m going to post an excerpt of a tutorial I created for the use of Moq on an internal company wiki for training purposes. I presented today on mocking for unit tests and was revisiting some of my old documentation, which included an introductory, unofficial explanation of lambda expressions that doesn’t involve any kind of mathematical lingo about lambda calculus or the term “first class functions”. In short, I created this to help someone who’d never or rarely seen a lambda expression understand what they are.


Since Moq makes heavy use of lambda expressions, I’ll explain a bit about how those work here. Lambdas as a concept are inherently highly mathematical, but I’ll try to focus more on the practical aspects of the construct as they relate to C#.

In general, lambda expressions in the language are of the form (parameters) => (expression). You can think of this as a mapping and we might say that the semantics of this expression is “parameters maps to expression”. So, if we have x => 2*x, we would say “x maps to two times x.” (or, more generally, number maps to number).

In this sense, a lambda expression may be thought of in its most practical programming sense as a procedure. Above, our procedure is taking x and transforming it into 2*x. The “maps to” semantics might more colloquially be called “becomes”. So, the lambda expression x => 2*x translates to “x becomes 2 times x.”

Great. So, why do this? Well, it lets you do some powerful things in C#. Consider the following:

Here, we have a function that will filter an int value out of a list. It’s a pretty handy function, since it lets you pick the filter value instead of, say, filtering out a hard-coded value. But, let’s say some evil stakeholder comes along and says “well, that’s great, but I want to be able to filter out two values. So, you add a method overload that takes two filters, and duplicate your code. They then come along and say that they want to be able to filter out three values, and they also want to be able to filter out values that are less than or greater than a specified value. At this point, you take a week off because you know your code is about to get really ugly.

Except, lambda expressions to the rescue! What if we changed the game a little and told the stakeholder, “hey, pass in whatever you want for criteria.”

Now, you don’t have to change your filter code at all, no matter what the stakeholder asks for. You can go back and say to him, “hey, do whatever you want to that integer — I don’t care”. Instead of having him pass you an integer, you’re having him pass you something that says, “integer maps to bool” or “integer becomes bool”. And, you’re taking that mapping and applying it to each element of the list that he’s passing you. For elements being filtered out, the semantics is “integer becomes false” and for elements making the cut “integer becomes true”. He’s passing in the mapping, and you’re doing him the service of applying it to the elements of the list he’s giving you.

In essence, lambda expressions and the mappings/procedures that they represent allow you to create algorithms on the fly, ala the strategy design pattern. This is perfect for writing code where you don’t know exactly how clients of your code want to go about mapping things — only that they do.

As it relates to Moq, here’s a sneak peak. Moq features expressions like myStub.Setup(mockedType => mockedType.GetMeAnInt()).Returns(6);
What this is saying behind the scenes, is “Setup my mock so that anyone who takes my mocked type and maps it to its GetMeAnInt() method gets a 6 back from it” or “Setup my mock so that the procedure MockedType.GetMeAnInt() returns 6.”

(By the way, the link I used for the visual is from this post, which turned out to be a great find. RSS feed added to my reader.)


Basic Spring MVC spring-servlet.xml Configuration

Tonight, I enjoyed a nice success. Specifically, I enjoyed the kind of success that I’ve found tends invariably to arise from using TDD — I wired some things together and discovered that everything just worked (well, at least my java code did – I did have a slight oops with javascript typos, but that’s to be expected in an environment where I get no feedback until runtime). And, what made this extra sweet is that I’m designing a server that turns lights on and off in my house. This means that at the eureka, breakthrough moment, you don’t find out from a running application or a successfully parsed file or anything as mundane as that. You’re treated to your house lighting up like a Christmas Tree to celebrate your success! (And then you’re thankful that the “off” also works because your sleeping girlfriend is probably not amused by this development.)

But, my purpose here is neither to gloat nor to stump for TDD. Instead, I wanted to give a nod to how easy it was for me to wire things up in Spring MVC 3, and what an improvement I perceive this to be from some years and versions back. Since I was doing TDD, I was basically isolating two classes that I have collaborating them and testing them individually. These classes are LightManipulationServiceHeyuImpl, which implements LightManipulationService and LightController:

This was all tested and looking good for the time being, so I figured I’d take a break from implementation and, well, see if any of it actually worked. Up until this point, I hadn’t bothered with any wireup, so I figured this would be an adventure. But, it wasn’t. A little google-fu and everything worked. I figured it would be easy enough to declare beans to do setter injection (I remembered this from the Spring MVC 1 days), but I thought that I might get snagged a little with constructor injection. I thought I might get snagged a lot with the fact that I wanted to inject the result of the static method Runtime.getRuntime() into my service.

But, I had no trouble in either case.

The first thing I set up was my service, using the familiar bean id and class syntax. From here, I located the constructor injection tag, but decided to come back to it since I thought the static method was going to be ugly. I then created the lightController bean and this is when I found the syntax for the constructor injection tag: constructor-arg. I specified which 0-indexed constructor argument I was supplying and referred it to my service bean. Simple enough. I don’t know whether the index is necessary with only one parameter or not, but hey, it’s working. I’ll figure that out when I need to.

From there, the static thing was surprisingly and pleasantly easy. I don’t know whether Runtime.getRuntime() is actually considered a factory method or not, but by using it in this fashion, I was able to accomplish what I wanted. This is going to come in extremely handy for cases where I have to pull things out of some framework or library static state and I don’t want to take that inline dependency to impede flexibility/testability.

And, really, that was it. I fired this up with my unit tested classes and absolutely nothing happened. I peered at the JSP pages and the javascript in them, realized I had forgotten a comma, fired again, and was dazzled by the lightshow in my house. So kudos to Spring MVC. Easy and flexible is always nice.


JUnit for C# Developers 6 – Cart Before the Horse

In this post, I’d like to point out something I learned while working following yesterday’s post. In my haste to find the JUnit equivalent of MS Moles, I didn’t stop to think about what I was doing.

So, as I expanded on yesterday’s effort, I realized that my mocking of Runtime.getRuntime() didn’t seem to be working properly. As I set about trying to fix this, something dawned on me. Runtime.getRuntime() returns a Runtime object, and it’s that object’s exec(string) method that I’m interested in. So, in the code that I was trying to test, I was engaging in a double whammy of a Law of Demeter violation and inlining a static dependency.

I believe I was distracted, as I mentioned, by my desire to find C# equivalents in Java and by the general newness of what I was doing. But, this is a “teachable moment” for me. It’s easy to slip into bad habits when things are unfamiliar. It’s also easy to justify doing so. When I realized what I was doing, my first thought was “well, give yourself a break, Erik — just go with it this way until you’re more comfortable.” I then shook off that silly thought and resolved to do things right.

It’s easy to follow good design principles when you’re following a tutorial or being taught. But, it’s imperative to do it all the time so that it becomes a reflex. This includes when you’re tired late at night and just wanting to turn off your downstairs light without going downstairs (my situation now). It includes when you’re behind schedule and under the gun on a project. It includes when people are giving you a hard time. It’s always. If you practice doing it right — make it rote to do it right — then that’s what you’ll do by default.

So, humbled, here is my updated code:


and class under test:

Obviously, I don’t want to execute the shell command “command”, but that’s tomorrow’s TDD. I’m happy for the evening, now that I’ve refactored an inline static Law of Demeter violation out of my design plans. ūüôā


JUnit for C# Developers 5 – Delving Further into Mocking

Today, I’m continuing my series on unit testsing with JUnit with a target audience of C# developers.


These are today’s goals that I’m going to document:

  1. See about an NCrunch-equivalent, continuous testing tool for Eclipse and Java
  2. Testing the various complexities of the @PathVariable annotations
  3. Use mockito to perform a callback
  4. Mocking something that’s not an interface

On to the Testing

The first goal is more reconnaissance than anything else. I have come to love using NCrunch (to the point where I may make a post or series of posts about it), and I’d love to see if there is a Java equivalent. NCrunch uses extra cores on your machine to continuously build and run your unit tests as you work. The result is feedback as you type as to whether or not your changes are breaking tests. The red-green-refactor cycle becomes that much speedier for it. My research led me to this stack overflow page, and two promising leads: infinitest and ct-eclipse (presumably for “continuous testing”). I’m pleased with those leads for now, and am going to shelve this as one of the goals in a future post. Today, I just wanted to investigate to see whether or not that was an option, and then move onto concrete testing tasks.

Next up, for Spring framework, my toggleLight method’s parameters need to be decorated with the @PathVariable attribute, which apparently allows delimited strings in the Request Mapping’s value to be mapped to parameters to the method. In this fashion, I’m able to map a post request REST-style URL to a request for toggling a light. To accomplish this, I studied up and wrote the following test:

This failed, of course, and I was able to make it pass by updating my code to:

Note the @PathVariable annotations. I’m no expert here, but as I understand it, this takes variables in the mapping’s value delimeted by {} and maps them to method parameters. In order to do this, however, the parameters need this annotation. So cool, I can keep doing TDD even as I add the boilerplate for Spring MVC.

At this point, however, I want to verify that the service is being invoked with parameters that actually correspond to toggleLight’s arguments. Right now, we’re just hardcoding null for the light. (Between last post and this one, I did some garden variety TDD using the Mockito verify() previously available in order to resolve the logic about passing true or false to the service for the light’s value). Using verify(), I can make sure that I’m not passing a null light, but I have no means of actually inspecting the light. In the C#/Moq TDD world, to get to the next step, I would use the Moq .Callback(Action) functionality. In the Mockito/Java world, this is what I found:

I’m creating an ArgumentCaptor object for lights and passing captor.capture() to verify(), which seems to work some magic for populating the captor’s value property with the light object passed to the service. I made this test pass, and then wrote another one for the light name, and wound up with the following code:

I don’t know that this counts as a callback, but it is the functionality I was looking for.

So, at this point, I’m temporarily done with the controller. Now, I want to implement the the service and have it make calls to Runtime.getRuntime.exec(). But, in order to do that, I need to be able to mock it. As you know, in C#, this is the end of the line for Moq. We can use it to mock interfaces and classes with virtual methods, but static methods and other test-killers require Moq’s more powerful, heavyweight cousin: the isolation framework (e.g. Moles). So, I scurried off to see if Mockito would support this.

I did not have far to look. The Mockito FAQ offered the following as limitations of the tool: cannot mock final classes, cannot mock static methods, cannot mock final methods, cannot mock equals(), hashCode(). So, no dice there. We’re going to need something else. And, almost immediately, I stumbled on PowerMock, billed as an extension to Mocktio. “PowerMock uses a custom classloader and bytecode manipulation to enable mocking of static methods, constructors, final classes and methods, private methods, removal of static initializers and more.” You had me at “mocking of static methods.”

So, I downloaded powermock-mockito.1.4.11-full.jar and slapped it in my externaljars directory along with Mockito. As it turns out, I needed more than just that, so I downloaded the full zip file from the site, which was in a file named “powermock-mockito-testng-1.4.11.zip”. I ran into runtime errors without some of these supporting libraries. From here, I poked and prodded and experimented for a while. The documentation for these tools is not especially comprehensive, but I’m used to that in C# as well. This is what wound up working for me, as a test that my service was invoking the runtime’s executable:

In the first place, I’d forgotten how much I loathe java’s checked exceptions, for all of the reasons I always did previously and now for a new one — they’re a headache with TDD. I mention this because I apparently need to have my test method throw that exception so that I can mock the runtime. (Not even use it — mock it). The rest of the stuff in there, I learned by experimentation. You have to include some new annotations, and you have to setup PowerMockito to mock the static class. From there, I created a mock of what Runtime.getRuntime() returns (not surprisingly, it returns a Runtime). Then, I setup the mock Runtime to toss an exception when its exec method is called — the one that I plan to use. I then expect this exception in the test. This is my clever (perhaps too clever) way of verifying that the exec() method is called in my class, without having tests that actually go issuing shell commands. That’d be a big bucket of fail, but I’d still like to test these classes and use TDD, so this is how it has to be.

Looking Ahead

Next time, I’ll work my way through developing this service and document anything that comes up there. These mocking frameworks are new to me, so it’s going to be a work in progress. I may or may not play with some of the continuous testing tools as well.


Are Unit Tests Worth It?

The Unit Test Value Proposition

I gave a presentation yesterday on integrating unit tests into a build. (If anyone is interested in seeing it, feel free to leave a comment, and I’ll post relevant slides to slideshare or perhaps make the power point available for download). This covered the nuts and bolts of how I had added test running to the build machine as well as how to verify that a delivery wouldn’t cause unit test failures and thus break the build. For background, I presented some statistics about unit testing and the motivations for a test-guarded integration scheme.

One of the questions that came up during Q&A was from a bright woman in the audience who asked what percentage of development time was spent writing unit tests for an experienced test writer and for a novice writer. My response to this was that it would be somewhat slower going at first, but that an experienced TDD developer was just as fast doing both as a non-testing developer in the short term and faster in the long term (less debugging and defect fixing). From my own personal experience, this is the case.

She then asked a follow up question about what kind of reduction in defects it brought, and I saw exactly what she was driving at. This is why I mentioned that she is an intelligent woman. She was looking for a snap-calculation as to whether or not this was a good proposition and worth adopting. She wanted to know exactly how many defects would be avoided by x “extra” days of effort. If 5 days of testing saved 6 days of fixing defects, this would be worth her time. Otherwise, it wouldn’t.

An Understandable but Misguided Assessment

In the flow of my presentation (which wasn’t primarily about the merits of unit testing, but rather how not to break the build), I missed an opportunity to make a valuable point. I wasn’t pressing and trying to convince people to test if they didn’t want to. I was just trying to show people how to run the tests that did exist so as not to break the build.

Let’s consider what’s really being asked here. She’s driving at an underlying narrative roughly as follows (picking arbitrary percentages):

My normal process is to develop software that is 80% correct and 20% incorrect and declare it to be done. The 80% of satisfied requirements are my development, and the 20% of missed requirements/regressions/problems is part of a QA phase. Let’s say that I spend a month getting to this 80/20 split and then 2 weeks getting the rest up to snuff, for a total of 6 weeks of effort. If I can add unit testing and deliver a 100/0 split, but only after 7 weeks then the unit testing isn’t worthwhile, but if I can get the 100/0 split in under 6 weeks, then this is something that I should do.

Perfectly logical, right?

Well, almost. The part not factored in here is that declaring software to be done when it’s 80% right is not accurate. It isn’t done. It’s 80% done and 20% defective. But, it’s being represented as 100% done to external stakeholders, and then tossed over the fence to QA with the rider that “this is ‘done’, but it’s not done-done. And now, it’s your job to help me finish my work.”

So, there’s a hidden cost here. It isn’t the straightforward value proposition that can be so easily calculated. It isn’t just our time as developers — we’re now roping external stakeholders into helping us finish by telling them that we’ve completed our work, and that they should use the product as if it were reliable when it isn’t. This isn’t like submitting a book to an editor and having them perform quality assurance on it. In that scenario, the editor’s job is to find typos and your job is to nail down the content. In the development/QA work, your job is to ensure that your classes (units) do what you think they should, and it’s QA’s job to find integration problems, instances of misunderstood requirements, and other user-test type things. It’s not QA’s job to discover an unhandled exception where you didn’t check a method parameter for null — that’s your job. And, if you have problems like that in 20% of your code, you’re wasting at least two people’s time for the price of one.

Efficiency: Making More Mistakes in Less Time

Putting a number to this in terms of “if x is greater than y, then unit testing is a good idea” is murkier than it seems because of the waste of others’ time. It gets murkier still when concepts like technical debt and stakeholder trust of developers are factored in. Tested code tends to be a source of less technical debt given that it’s usually more modular, maintainable, flexible, etc. Tested code tends to inspire more confidence in collaborators as, you may run a little behind schedule here and there, but when things are delivered, they work.

On the flipside of that, you get into the proverbial software death march, particularly in less agile shops. Some drop-dead date is imposed for feature complete, and you frantically write duct-tape software up until that date, and then chuck whatever code grenade you’re holding over the QA wall and hope the shrapnel doesn’t blow back too hard on you. The actual quality of the software is a complete mystery and it may not be remotely close to shippable. It almost certainly won’t be something you’re proud to be associated with.

One of my favorite lines in one of my favorite shows, The Simpsons, comes from the Homer character. In an episode, he randomly decides to change his name to Max Power and assume a more go-getter kind of identity. At one point, he tells his children, “there are three ways of doing things: the right way, the wrong way, and the Max Power way.” Bart responds by saying, “Isn’t that just the wrong way?” to which Homer (Max Power) replies, “yes, but faster!”

That’s a much better description of the “value” proposition here. It’s akin to being a student and saying “It’s much more efficient to get grades of C and D because I can put in 10 hours per week of effort to do that, versus 40 hours per week to get As.” In a narrow sense that’s true, but in the broader sense of efficiency at being a good student, it’s a very unfortunate perspective. ¬†The same kind of nuanced perspective holds in software development. ¬†Sacrificing an objective, early-feedback quality mechanism such as unit tests in the interests of being more “efficient” just means that you’re making mistakes more efficiently. ¬†And, getting more things wrong in the same amount of time is a process bug — not a feature.

So, for my money, the idea of making a calculation as to whether or not verifying your work is worthwhile misses the point. ¬†Getting the software right is going to take you some amount of time X. ¬†You have two options here. ¬†The first option is to spend some fraction of X working and then claim to be finished when you’re not, at which point you’ll spend the other portion of the fraction “fixing” the fact that you didn’t finish. ¬†The second option is to spend the full time X getting it right.

If you set a standard for yourself that you’re only going to deliver correct software, the timelines work themselves out. ¬†If you have a development iteration that will take you 6 weeks to get right, and the business tells you that you only get 4, you can either deliver them “all” of what they want in 4 weeks with the caveat that it’s 33% defective, or you can say “well, I can’t do that for you, but if you pick this subset of features, I’ll deliver them flawlessly.” ¬†Any management that would rather have the “complete” software with defect landmines littering 33% of the codebase than 2/3rds of the features done right needs to do some serious soul-searching. ¬†It’s easy to sell excellent software with the most important 2/3rds of the features and the remaining third two weeks out. ¬†It’s hard to sell crap at any point in time.

So, the real value proposition here boils down only to “do I want to be adept at writing unreliable software or do I want to be adept at writing software that inspires trust?”