If it ain’t broke, don’t fix it. Or, what?

When I  promised to write about how to reduce friction from developers’ work day, I am sure that most expected me to focus on what non-developers ought to do in order to improve the working lives of developers.

But let’s face it, the major cause of friction, crappy legacy code, is our own fault. Developers wrote that crappy code, after all. So let’s focus on what we can do about that.

Software developers have a natural tendency to keep the hands off code that already works. After all, conventional wisdom tells us that it is a bad idea to try to fix something that works.

Such conventional wisdom often makes a lot of sense, even in the world of software development; any change involves an element of risk, and you surely do not want to risk introducing bugs unless you need to.

On the other hand, we need to think about the risk of not changing code. This risk is often overlooked, so I was happy that Martin Fowler in a recent article on the second edition of his Refactoring book reminded us that he was impressed while working with Kent Beck because,

[…]one thing that really stood out was they [sic] approach he took to continually reworking the code base to keep it healthy[…]

I strongly suggest that serious software developers dig into the wisdom of those two gurus (Martin Fowler and Kent Beck) in order to understand why code which is not continually trimmed will actually rot.

In my humble opinion, there are numerous reasons for constantly trimming code. For example, new requirements cause existing code to get in the way of changes, or as time goes by you get wiser on the underlying problem you are trying to solve. In short: When you get wiser, use that wisdom to continually improve your code.

If you don’t continually trim your code, if you postpone needed refactoring too long, then you end up with unmanageable code that you wish you could rewrite from scratch. Rewriting from scratch might be the only feasible option, but you will probably nevertheless have to focus your time elsewhere.

Don’t get me wrong here. I have written code which still runs unchanged after many years without needing a change. In the rare cases when this happen, don’t change the code just for the sake of a principle.

Still, in conclusion, even when code works, if the way the code is crafted is no longer good enough, you really should change it.



Building a Safety Net for Continuous Delivery with Developer Tests

It is impossible to develop a software system with a certain level of complexity unless it is built on top of a smaller working system.

I wanted to credit Bjarne Stroustup for expressing this point of view as early as 1985 in his book The C++ Programming Language, but after re-reading his Notes to the Reader, I see that the quote I remembered was on the importance of well-structured code (in which C++ excels over C), not correctness.

Still, I don’t think I am alone when I claim that we software developers find it natural to develop iteratively, thereby continuously building on top of the last iteration, the last working system.

The question is, how do we know that the system we build on is working?

The truth is that unless we have a very good verification process we don’t know if we build on a working system.

In a good continuous delivery process, we will have waves of verification in the form of continuous integration builds and deployments, automatic and manual testing by testers etc.

Naturally, developers will also develop unit tests as an integrated part of developing code, thereby ensuring that each implemented responsibility behaves as expected in isolation.

But is this good enough? Will it ensure that each iteration builds on a working system?

I think it is not good enough because,

  • Testing is usually decoupled in time and space from the development process.
  • Unit testing only verifies tiny pieces of logic in isolation, but bugs typically show up when these pieces of logic are composed into higher level behaviour.

If you ask me, developers needs to write what I call developer tests.

Developer Tests

A developer test is similar to a unit test, the difference being that we never mock any dependencies unless we absolutely must. For example, we mock external web services that our code calls, but we do not mock database access.

When we run a developer test, we run the exact same code as is run in the production system, which means that the behaviour of the test will closely match the behaviour of the production system. This means that the verification which is done by a developer test is very reliable.

When I develop new code, I always exercise the new behaviour through developer tests. This is typically much easier than setting up the production system with the relevant users with relevant permissions and relevant data to query and alter.

When my new developer tests turn green, I feel confident that my new behaviour works as intended, not only in isolation but also when run in context with huge parts of the existing functionality.

When I have verified that the existing developer tests are green I feel confident that I did not introduce regressions.

Then I check the code changes into the main branch and the new feature will be in the next release a short while after.

What Makes Developer Tests Work

I have developed the concept of developer tests over the last couple of years while working on TradingFloor.com. Since it is now second nature to use developer tests as an integral part of the software development process, it is difficult to remember why this seemed difficult, or impossible, to do just a couple of years ago.

A major part of the reason that developer tests work in TradingFloor.com is that the code is (largely) written with sensible principles in mind, and in this context one of the SOLID principles, the Dependency Inversion Principle (DIP), is essential. And furthermore, using Dependency Injection is practical.

This means that when I exercise my new behaviour through the method Foo on class Bar …

public class Bar
Bar(IMyDependency1 dep1, IMyDependency2 dep2) { /*…*/}
void Foo() { /*… use dep1 and dep2 */ }

… then I also run the code of the two dependencies (and their dependencies, and their dependencies …), including any kind of logging, interception and whatnot. This is in contrast to a unit test in which I would mock the two dependencies.

In addition to DIP, our experience is that the Command Query Separation (CQS) principle is a great help in general in our code structure, and in particular this principle makes writing developer tests easy. I suppose you can imagine that a code base composed of queries (we call them readers) and command handlers are very handy when building up a test scenario and when asserting the outcome of a test.

Why is the Entire World not Using Developer Tests

Developer tests allow for faster development, they provide fast feedback on correctness during development and they provide a safety net for the future.

Yet, I have not seen a rush for all other developers to get on board and start to use developer tests. Why?

Here are some of the counter arguments I have heard so far,

  • It cannot be done.
    That argument is a couple of years old. Today we are doing it on a daily basis.
  • It is too slow.
    No, our 850+ tests run in one minute on a typical developer PC.
  • Developer tests are very brittle.
    No, it is the other way around. Unit tests are often very brittle because you need to re-do your mocking when refactoring code. Developer tests don’t have this problem and they are surprisingly solid towards refactoring.
  • I cannot do it because my code is much more complex than your code.
    If your code is really complex, working without a safety net is not an option! You can do it.
  • I run a heavy SQL database, tests will be too slow and difficult to set up.
    Right, we run a no-SQL database so building up an entire database per test is fast and easy. Installing the database locally and on any build or test system is also easy and fast. All that will be a hassle with some SQL databases, but not impossible. If you have to, you can isolate SQL access and mock it out but I would prefer not to.

Where Are We Now

I would love to share more details but I feel that I need to introduce developer tests to at least one more project before I can express myself without going into too much detail.

I will come back with more information once I have done that. In the meantime, if you would like me to elaborate on this or that, please ask.

The ABC of Continuous Delivery

You probably know the triple L of real estate: location, location and location.

In the same way some would argue that the three most important focus areas, the triple A of continuous delivery, are automation, automation and automation. While I believe there is some truth to that, I am even more convinced that it’s not AAA, but rather ABC.

The A is obviously automation. Read the book, and you will see why it is so obvious.

The B is obviously baby steps. Read my last blog post, and you will see why it is so obvious. I will blog more about that later.

The C is not-so-obviously clean code. You didn’t see that one coming, did you? It could also mean consistency, which might seem to be a sub-set of clean code, but if you ask me it is much more than that and also much more important. I will blog more about that later.

Why is Clean Code so Important?

When I write about clean code in this context it is mainly in the Uncle Bob sense. Read the Clean Code book, and you will know what I mean.

You may wonder why it is so important to focus on the nitty-gritty details of code structure. Isn’t the larger picture of patterns and practices more important?

The answer to that is, yes, patterns and practices are absolutely infinitely more important. But keeping the code clean is important for the simple reason that you cannot write code unless you read existing code, so in order to efficiently write code, the code already written must be readable. And code that is structured strictly according to agreed upon rules is more readable than less consistent code.

While I am a fan of Uncle Bob, I am also sceptic about coding standards that cannot be automated. So, I must admit that the code guidelines I follow for my C# code is largely determined by close to 100% default ReSharper settings + a handful of StyleCop rules. These rules are sensible, they are automated and thus really easy to follow. They do not automate all aspects of clean code, but they go far in that direction. I also use a plugin to ReSharper for spell checking, as it helps me to semi-automatically use meaningful identifiers.

For a team that consistently follows such agreed upon and automated rules the benefit is obvious, since a developer will feel at home in any parts of the code, regardless who wrote it (well, honestly it takes a bit more effort to fully achieve that, but I will blog about that later). This is important for a team that needs to continuously deliver.

How does this Relate to Lean Manufacturing?

Some will argue that Lean Software Development and Lean Manufacturing differ in at least one important aspect.

For Lean Manufacturing, it is important that any kind of variation is minimized, since variation tends to create a ripple effect that will cause trouble in a physical factory. This effect is described in an easy to understand fashion in the two novels I mentioned in my last blog post.

But software is different, right? Minimizing variation would kill creativity and all software projects are so different and novel that it would be futile to fight variation, right?

If you ask me, we must fight unneeded variation in code. If you insist on placing curly brackets in your own way, and in general follow you own style instead of a style agreed upon by the team, then you create unneeded variation which will cause you team mates to be less efficient.

Instead, spend your brain cycles on whatever makes you product valuable. If you don’t know what that might be, find yourself another job.

Notice how fighting unneeded variation goes hand-in-hand with automation. Another example of this is the use of an automation tool that you already use every day – the compiler – to write code without compiler warnings. Compiler warnings can actually make a lot of sense and if you ignore some, you might accidentally one day ignore one of the really sensible compiler warnings.

On my team, we have set the warning level to the highest possible, and defined that warnings should be treated as errors.

How to get There

If you write new code in a new project, it is a no-brainer to write clean code from day one.

If you work on legacy code, as most of us do most of the time, you need to think a bit about how to get there. Do you change all the code in one revolutionary check-in? Do you evolve the code by only following the guidelines for new code? Do you clean existing code only if you need to make significant changes to it?

If you ask me, you might as well clean up as much code that you can automatically, if you have a tool that you trust. Once you need to do manual changes, you might break otherwise working code. Then you need to think a bit more about the process and be cautious – especially if you don’t have a safety net of automated tests.

That’s It

All this is really a no-brainer. Just do it, gain the benefit – and be prepared for the next level of clean code in which you consistently apply agreed upon patterns and practices.

On the other hand, if you find this level of clean code to be really hard to achieve for a team and its source code, you should consider if this team really is prepared to embark into the world of continuous delivery.

Continuous Delivery

How does the topic Continuous Delivery fit in a blog which is mainly on code architecture and code crafting?

In this post I hope to convince you that it fits quite well.

I am working on a team which has successfully practiced continuous delivery for some time and it turned out that pretty much everything we did affected our ability to actually deliver continuously.

In this context everything literally means everything in the software development life-cycle, from stated business goals to provable value for the customers. This does not only involve crafting the code but also deriving scope from business goals in a way that can guide the further process, which includes various kinds of testing and verification in parallel with coding, as well as operations tasks. Continuously.

The bad part is that if you wish to practice continuous delivery you need to rethink everything you do and you need to break quite a few habits. The good part is that it does not involve radically new practices, you only need to take existing and well-known practices more seriously and apply them consistently. Continuously.

It is common knowledge that the way we structure code, the way we break down complexity into manageable bits and the way we apply well-understood and agreed upon patterns consistently greatly affects our ability to deliver. So it is not a surprise that when we need to continuously deliver with a short cycle time, then all this becomes even more important.

What Is Continuous Delivery?

The short answer to that is look it up or read the book.

If you ask me, continuous delivery is really Lean Manufacturing principles applied to software development. The principles from physical manufacturing have been modified slightly in order to make sense in the software development world.

Do Lean Manufacturing Principles and Processes Really Fit With Software Development Ditto?

It is surprising how many physical manufacturing processes fit nicely with software development processes.

In lean manufacturing you want to have a short cycle time, meaning the time from feeding raw material into the factory and until the product is finished (and by then the product is hopefully valuable to the target user). This sounds awfully similar to what we want to achieve with continuous delivery, doesn’t it?

One way to achieve a short cycle time is to produce small batches, i.e. producing only a small number of items of a given type before switching to making another type of item. The challenge with small batches is that it takes significant time to set-up machines between two batches. And does it make sense to produce, say, 10 items in two hours, then spend 2 hours setting up a machine in order to produce 10 items of another type in two hours etc.? Wouldn’t it be better to produce 1000 items per batch, thus making the set-up time relatively small? The answer is that, yes it probably makes sense to have a small batch size, and no building up a large inventory is probably not a good idea. Optimize the process of setting up machines rather than increasing the batch size thereby avoiding the large amount of Work In Process (WIP) at any given time. The rationale is really quite simple and the logic makes a lot of sense. If you don’t believe me, I suggest you read the novel The Gold Mine: A Novel of Lean Turnaround. Yes, it’s a novel so it doesn’t really feel like working when you read it. But it will give you a gentle introduction to lean principles in manufacturing. (If you get really fired up on this topic I suggest you also read The Goal: A Process of Ongoing Improvement. It will teach you about the importance of focusing on bottle necks in the process.)

In the world of software development we have a similar challenge with batch sizes, the batch size in this context being the amount of code that we deliver (or anything else we deliver, but let’s focus on code for now).

We see the batch size challenge at multiple levels. At the highest level, the business would like us to turn the business goals into value for the customer as fast as possible, and one way to do that is to initially focus narrowly on minimal functionality. That’s a no-brainer, you say, but the business will never accept it – they always want it all and the want it yesterday, right? Well, it can be done. As Eric Ries describes in The Lean Startup: How Today’s Entrepreneurs Use Continuous Innovation to Create Radically Successful Businesses it makes a lot of sense to quickly make a Minimal Viable Product (MVP) that can be validated and used in the further process. That is a small batch size which is used to achieve a short cycle time in software development!

We also see the batch size challenge for individual check-ins into a version control system and when merging change sets among branches. If you are really continuous you do continuous deployment and each check-in will be deployed directly to the live production system. This is not as risky as you might think if you have automated as much as possible, including tests, and if you can live with occasional hick-ups which you need to address fast. In my team we do not deploy each check-in, although we do deploy often after a short manual verification process that augments all our automated processes.

My advice here is to always strive at delivering baby steps, meaning small focused check-ins and small focused features. Any process which is in the way of doing baby steps must be optimized. If gated check-in takes 6 hours on a good day, then find another way to check the code. If code reviews have response times of several hours or days you need to look into that part of the process. If testing is a bottle-neck you need to address that, probably by adding resources in the short-term and doing more automation in the long-term (so that manual testing can be focused on new and UI centric features). Our goal must be to have as little WIP as possible, which in this context means code that we have spent time on but which has not yet been fully verified and turned into value for actual users.

That’s It

It is really that simple, deliver baby steps quickly, optimize any process that prevents you from doing that, automating as much as possible on the way.

But even though it is simple to state, it is not always easy to do it. I am writing primarily to code craftsmen, but before we go deep into core coding topics, you should convince those who control your process to read on Lean Manufacturing and Lean Startup. And if they get really fired up, they should also read Specification by Example: How Successful Teams Deliver the Right Software. In fact you, the code craftsman, and your testers should also read it – it could potentially help all three disciplines to work better together.

For true continuous delivery to work, developers must accept to be part of the full process, so a developer must accept to also partly work with operations. Read The Phoenix Project: A Novel About IT, DevOps, and Helping Your Business Win and accept that we are no longer mere developers – from now on we are DevOps.

This was a long introduction. In my next post I will go deeper into how to craft code in the world of continuous delivery. I hope that by now you agree that successful continuous delivery requires that we think differently about the entire software development process. After my next few posts I hope you agree that we also need to think about code structure differently.

Then again, maybe you already do. After all, the coding practices I am going to describe are all based on existing knowledge and generally accepted practices, so maybe you do it all already.

Even More Asserts in a Single Unit Test Method

In my last post I stated that sometimes it is OK to have multiple asserts in a single unit test method and I devised a helper class MultiAssert for that.

Please do not get me wrong – I am a great believer in keeping unit tests readable and maintainable, and restricting a unit test to have exactly one assert is one way to achieve that.

But despite that I am now going to argue that sometimes I find it to be OK to have multiple asserts in a unit test method, even without packing all the asserts up using MultiAssert.Aggregate.

Remember that “The arguments against multiple asserts are multiple, a main one being that if one assert fails the rest will not be executed” (from my last post).

However, what if I in this case explicitly want the rest of the unit test not to execute – is it then OK to have one assert ensure that another is not executed? I think so. Read on and I will explain.

The other day, I looked into a unit test that failed randomly. I knew that a person whose unit testing skills I do not usually question wrote it. Still, it turned out that it was surprisingly tricky for me to figure out why it failed.

The following is a simplification of the original unit test,

public void DoThis_MustCallDoThatOnFooWithExpectedParameters_WhenCalled()
_target.Initialize(_foo, "first", "second");
f => f.DoThat(
Arg<string>.Is.Equal("first"), Arg<string>.Is.Equal("second")));

In order to figure out why this unit test failed, I started making assumptions.

My first assumption was that DoThat was called with at least one of the two parameters having an unexpected value, so I added a line to the unit test to let Rhino tell me what the actual parameter values for the DoThat call was,

var actualArgs = _foo.GetArgumentsForCallsMadeOn(f => f.DoThat("", ""));
() => Assert.AreEqual("first", actualArgs[0][0]),
() => Assert.AreEqual("second", actualArgs[0][1]));

This did not help me, as inspecting actualArgs only caused an index was out of range exception to be thrown.

My second assumption was that DoThat was not called at all. To test this hypothesis I tried the following,

_foo.AssertWasCalled(f => f.DoThat(Arg<string>.Is.Anything, Arg<string>.Is.Anything));

Bingo! The unit test still failed (randomly) with this assert, which showed me that DoThat was not called.

So the single assert of the original unit test was actually multiple asserts behind the scene – one assert to state that DoThat was called and two asserts to state that each of the two parameters were as expected. This is one case of having multiple asserts that I do not like!

With this knowledge, it was quite easy to track down the root cause of the failure. Somebody had checked in a parallel implementation of _target.Initialize so that the initialization of _target randomly made it to completion before _target.DoThis was called. While I realize that it is important to figure out what this new parallel code will do in production code, for now I will keep focus on the correct implementation of this unit test method.

Since we have three asserts in this unit test, some would say that it is obvious to split it into three separate unit tests? Well, I would go for one or, perhaps, two. Read on and I will explain.

If we want to split into three unit tests, that would be

  • One to check if DoThat was called at all,
  • One to check that firstParam had the expected value, and
  • One to check that secondParam had the expected value

It could be argued that this is a lot of repeated setup that should be avoided. I do not agree – common setup can be put in auxiliary methods and execution will be very fast because I have mocked out external dependencies to the code under test.

My problem with the tree unit test approach is rather that the first unit test is a prerequisite to the two other unit tests; if the first fails, the two other will certainly also fail (barring random behaviour). I believe that if you have an assert that, if failed, will make another assert fail with certainty, then these two asserts can, and often must, be packed together into a single unit test method.

So does that mean that the correct implementation of this unit test is to split it into two unit tests?

  • One that checks if DoThat was called, and also checks that firstParam had the expected value, and
  • One that checks if DoThat was called, and also checks that secondParam had the expected value.

I believe that the most maintainable way to implement this is to have a single unit test.

  • One that checks if DoThat was called, and also uses MultiAssert to verify that both firstParam and SecondParam had the expected values.

All in all, this means that we have changed the original – hidden – multiple asserts into two explicit asserts. This is better than the original, as the first assert is a prerequisite of the second, which means that it makes no sense to execute the second if the first fails.

Here is the resulting implementation,

public void DoThis_MustCallDoThatOnFooWithExpectedParameters_WhenCalled()
_target.Initialize(_foo, "first", "second");


_foo.AssertWasCalled(f => f.DoThat(Arg<string>.Is.Anything, Arg<string>.Is.Anything));
var actualArgs = _foo.GetArgumentsForCallsMadeOn(f => f.DoThat("", ""));
() => Assert.AreEqual("first", actualArgs[0][0]),
() => Assert.AreEqual("second", actualArgs[0][1]));


Multiple Asserts in a Single Unit Test method

The title of this post is a provocation to many people who have read and love Roy Osherove’s brilliant book, The Art of Unit Testing. In this book Roy clearly states that one of the pillars of good tests is to avoid multiple asserts in a unit test.

The arguments against multiple asserts are multiple, a main one being that if one assert fails the rest will not be executed, which means that the state of the code under unit test is really unknown. Another argument is that if you find a need for having multiple asserts, it is probably because you are testing multiple things in a single unit test method. This will break the principle of single responsibility and maintainability will suffer.

I am a great believer in having maintainable and readable unit tests and I have always tried to follow the single assert advise myself. I am also a great believer in the principle of single responsibility, although I am often forced to be pragmatic when working on legacy code. When I want to test several outcomes from a single object I can choose to implement Equals, or maybe ToString in order to do direct comparisons of whole objects. Sometimes I will try to make a utility method or class that will allow me to compare several values in a way that will fit a single assert. While some people do not like adding to the code base for unit testing purposes only, most people object to having too many utilities creeping up in the unit test projects.

Recently I had discussions on unit testing with my colleagues and the reasoning behind single asserts came up – and also some arguments against it.

Let’s have a look at one of Roy’s examples,

public void CheckVariousSumResults()
    Assert.AreEqual(3, this.Sum(1001, 1, 2));
    Assert.AreEqual(3, this.Sum(1, 1001, 2));
    Assert.AreEqual(3, this.Sum(1, 2, 1001));

The problem here is that if one assertion fails, the rest will not be run and we do not know if they would fail if run.

There are a number of solutions to this.

The first solution: Create a separate test for each assert

This is easy and it only takes a few seconds to write those unit tests,

public void Sum_1001AsFirstParam_Returns3()
    Assert.AreEqual(3, this.Sum(1001, 1, 2));
public void Sum_1001AsMiddleParam_Returns3()
    Assert.AreEqual(3, this.Sum(1, 1001, 2));
public void Sum_1001AsThirdParam_Returns3()
    Assert.AreEqual(3, this.Sum(1, 2, 1001));

What is the problem with this solution?

Well, although the example may be slightly contrived it is easy to imagine that the three cases are somewhat correlated. By putting all three asserts in a single method we have signaled that these must be considered as a whole in order to be understood, while if we create separate unit test we have lost this information. And imagine that there were more than three cases, say 42? If a fundamental bug in the Sum method creeps in so that all 42 unit tests fail, would you prefer to have 42 unit tests fail or would you prefer to have a single unit test fail?

Another problem is maintainability. It is correct that it only takes a few seconds to write these three unit tests, but someone needs to maintain them in all future and the task can become daunting due to the sheer number of unit tests.

Both problems can to a certain extend be overcome with proper naming and with true single responsibility of units under test as well as each unit test method, but that is not always the reality – especially when you try to put legacy code under unit test.

The Second Solution: Use Parameterized Tests

In many cases I would prefer to use parameterized tests. However, currently my unit testing environment is Visual Studio 2010 and it does not support such a feature!

The Third Solution: Use try-catch

Since an assertion failure means that an exception is thrown, at least in the unit test frameworks I have used so far, we can simply catch such exceptions, do some intelligent processing, and then allow the next assertion to fail or succeed. That solves our problem with having multiple asserts.

Even though Roy is my unit testing hero, I think he is a bit too hasty to simply abandon the try-catch solution with a statement like "Some people think it’s a good idea to use a try-catch block […] I think using parameterized tests is a far better way of achieving the same thing."

A Simple Solution Using try-catch

Since I cannot write parameterized unit tests with my unit testing environment, I had to come up with an alternative solution. My solution is to introduce a new MultiAssert class which will accept delegates to multiple assert statements but only fail at most once. This new class seems to be a logical addition to the existing family of assert classes along with e.g. CollectionAssert and StringAssert.

Here is the above example in a single unit test with a single assertion,

public void CheckVariousSumResults()
        () => Assert.AreEqual(3, this.Sum(1001, 1, 2)), 
        () => Assert.AreEqual(3, this.Sum(1, 1001, 2)), 
        () => Assert.AreEqual(3, this.Sum(1, 2, 1001)));

MultiAssert.Aggregate can even be used in situations that do not fit parameterized unit tests easily.

Here is the implementation of MultiAssert.

public static class MultiAssert
    public static void Aggregate(params Action[] actions)
        var exceptions = new List<AssertFailedException>();

        foreach (var action in actions)
            catch (AssertFailedException ex)

        var assertionTexts = 
            exceptions.Select(assertFailedException => assertFailedException.Message);
        if (0 != assertionTexts.Count())
            throw new
                    (aggregatedMessage, next) => aggregatedMessage + Environment.NewLine + next));

Using or Abusing Multiple Asserts

MultiAssert can be abused, it is not meant as a universal excuse for cramming a lot of assertions into any unit test method. Remember that maintainability and readability of unit tests must still be a top priority and you should only use MultiAssert when this can be achieved.

One situation in which I recommend the use of MultiAssert is when it makes sense to assert both pre- and post-conditions in a unit test method. In this context, a post-condition is simply a (single) assert that states something about the state of the world after the Act part of the unit test method. However, if you assert that something has the value 42, how do you know that this was not already true right after the Arrange part of the unit test? After all, the Assert part of your unit test must assert what was supposed to happen as a consequence of the Act part of the unit test method.

So one nice usage of MultiAssert is to assert both pre- and post-conditions in unit tests.

public void Foo()
    // Arrange
    var underTest = ;
    bool preCondition = underTest.TheFoo() != 42;

    // Act
    int actual = underTest.TheFoo();

    // Assert
        () => Assert.IsTrue(preCondition),
        () => Assert.AreEqual(42, actual));

The Silverlight 3 SelectedIndex bug

There is a Silverlight bug which bothers a lot of users worldwide – if you bing the issue you will find quite a few hits.

The Problem

The problem is simple to state. Create a combo box and bind to the SelectedIndex and ItemsSource properties and you get an out of bounds error when a value is selected.

<ComboBox SelectedIndex="{Binding Path=IndexOfCurrentValue}" ItemsSource="{Binding Values}">

One of the sources that describe the problem is http://www.lhotka.net/weblog/SilverlightComboBoxControlAndDataBinding.aspx. This post includes a work around but I wanted to find a simpler solution.

I asked the Silverlight product team and the answer was that this bug is very unfortunate, but that it may not be trivial to fix.

In my repro project, I bound SelectedIndex before binding ItemsSource, so when the binding engine sets the SelectedIndex property, items are not set yet and the value 0 is not correct.

The Solution

The workaround is simple; simply switch the order of bindings in XAML and bind ItemsSource before binding SelectedIndex.

This is a reported bug and it is still open at the time of writing this. I hope it will be fixed by Silverlight 4.

WPF: How to mark an input field as not valid

I wanted to add to my input controls a visual clue that tells if the current content of the control is valid.

The visual clue I had in mind was a red squiggly line, similar to the way MS Word underlines my typos.

Why this particular visual clue? Because this is how it is done in MS Dynamics AX,


This left me with two tasks,

1. How to trigger the actual validation.

2. How to change the default WPF visual clue (a red border around the control) to the desired red squiggly line.

How to trigger validation

I thought about using validation rules similar to what Nigel Spencer blogged about. But validation is only triggered when the binding value has changed, while I wanted the visual clue to work even at start-up.

I was not alone with this problem. Willem Meints blogged about this as well as about several other validation problems. He actually wrote a complete validation framework that I found would be too much of a good thing for me. He also wrote that he did not use IDataErrorInfo as it does not provide enough flexibility.

I like to keep things simple, so I opted to use IDataErrorInfo.

Basically, I let the business object I bind to report an error if it is not happy about what the user typed so far. It can be augmented by validation rules, e.g. a user defined NumberRangeValue.

For the simple case, meaning the check for mandatory fields having a value, I use code similar to the following,

public string this[string columnName]
        if (string.IsNullOrEmpty(columnName))
            return string.Empty;

        if (<this is a mandatory field and has no value>)
            // This is never shown in the UI and does not
            // have to be localized; but if we ever choose
            // to show this text it must be localized!
            return "This field is mandatory.";

        return string.Empty;

Now I need to figure out how to trick the visual of controls to show a squiggly red line instead of the default red border.

How to get the red squiggly line

The TextBlock control can actually do the trick with the red squiggly line with its built-in spell checker. Alas, there is no API to trigger this at will.

Instead I found that the following XAML placed in a Resources section would do the trick,

<DrawingBrush x:Key="squiggleBrush" TileMode="Tile"
        Viewbox="0,0,4,4" ViewboxUnits="Absolute"
        Viewport="0,0,4,4" ViewportUnits="Absolute">
        <GeometryDrawing Geometry="M 0,2 L 1,1 3,3 4,2">
                <Pen Brush="Red" Thickness="1"
                StartLineCap="Square" EndLineCap="Square"/>
<ControlTemplate x:Key="SquiggleError">
<StackPanel HorizontalAlignment="Center" VerticalAlignment=
        <Rectangle Height="4" Fill=
            "{StaticResource squiggleBrush}"/>
For e.g. a combo box I apply this template as follows,
<ComboBox >
DynamicResource ResourceKey=”SquiggleError”/> </Validation.ErrorTemplate>
All this ensures that the red squiggly line is drawn when I want it, and it looks close enough to the real thing,


WPF: Making combo box items disabled – also when accessed using the keyboard

When I first embarked on WPF I ran into a number of small problems. Here is one of them.

The problem

I tried various ways to make my data bound combo box items disabled. I found that binding the ComboboxItem.IsEnabled to a property that indicates whether the item should be enabled did the trick.

But this did not work when I selected items using the keyboard. I also tried to make these not focusable but with the same result.

What worked: Click the combo down with the mouse and see that items that must be disabled can in fact not be selected.

What not worked: Use tab and arrow down to select items. They are not disabled and I can select them just fine with keyboard keys. Funny enough, if I click the combo down with the mouse, then the items are in fact disabled and cannot be selected with the keyboard keys.

Here is the XAML (I set up the data, meaning the properties FieldDomainValues, IsSelectable and ValueAsString, in constructers in code-behind):

<ComboBox x:Name="cmbx_test" ItemsSource="{Binding Path=FieldDomainValues}">
                <DataTrigger Binding ="{Binding IsSelectable}" Value="False">
                    <Setter Property="ComboBoxItem.Focusable" Value="False"/>
                    <Setter Property="ComboBoxItem.IsEnabled" Value="False"/>
            <StackPanel Orientation="Horizontal">
                <TextBlock Text="{Binding Path=ValueAsString }"></TextBlock>

What did I do wrong here?

The workaround

Before we dig into the core reason for this problem, let’s have a look at a simple work-around that Parag Bhand suggested,

We also faced similar issues while dealing with binding in combobox. Once we open the dropdown every thing works fine (even from keyboard) then onwards. I guess it has something to do with ItemsContainerGenerator because item containers are not generated until dropdown is opened at least once.

In fact if we open the drop down and close it again programmatically in window’s loaded event handler then also it works fine.

cmbx_test.IsDropDownOpen = true;
cmbx_test.IsDropDownOpen = false;

This is what I do now and it works.

The explanation

Dwayne Need, found the explanation,

ComboBox.SelectItemHelper has logic to determine if the item and its corresponding container allow selection. When there is no container, this logic always returns True. The containers (ComboBoxItems) aren’t created until the dropdown box is measured, which doesn’t happen until the user causes the dropdown to appear, typically by clicking on its down-arrow button.

If you followed that, it’ll be clear why the workaround works. It causes a measure on the dropdown, which generates the containers. After that, SelectItemHelper’s logic picks up the state the app has declared for the containers. It should also be clear why we don’t do that for you automatically – the workaround creates UI that is never displayed.

This looks like a scenario bug to me. While the technical details make sense, the end-to-end scenario is broken.

Please file a bug.

This has now been filed as a bug (750993: Unable to disable combo-box items when selected with the keyboard).

Unfortunately the fix for this bug will not make it into .NET 4.0 so we must try and get around with the workaround for now.