For example this article introduces them.
What is the benefit?
Static analysis seems cool but at the same time it would prevent the ability to pass null as a parameter in unit test. (if you followed the example in the article that is)
While on the topic of unit testing - given how things are now surely there is no point for code contracts if you already practice automated testing?
Update
Having played with Code Contracts I'm a little disappointed. For example, based on the code in the accepted answer:
public double CalculateTotal(Order order)
{
Contract.Requires(order != null);
Contract.Ensures(Contract.Result<double>() >= 0);
return 2.0;
}
For unit testing, you still have to write tests to ensure that null cannot be passed, and the result is greater than or equal to zero if the contracts are business logic. In other words, if I was to remove the first contract, no tests would break, unless I had specifically had a test for this feature. This is based on not using the static analysis built into the better (ultimate etc...) editions of Visual Studio however.
Essentially they all boil down to an alternate way of writing traditional if statements. My experience actually using TDD, with Code Contracts shows why, and how I went about it.
I don't think unit testing and contracts interfere with each other that much, and if anything contracts should help unit testing since it removes the need to add tedious repetitive tests for invalid arguments. Contracts specify the minimum you can expect from the function, whereas unit tests attempt to validate the actual behaviour for a particular set of inputs. Consider this contrived example:
public class Order
{
public IEnumerable Items { get; }
}
public class OrderCalculator
{
public double CalculateTotal(Order order)
{
Contract.Requires(order != null);
Contract.Ensures(Contract.Result<double>() >= 0);
return 2.0;
}
}
Clearly the code satisfies the contract, but you'd still need unit testing to validate it actually behaves as you'd expect.
What is the benefit?
Let's say that you want to make sure that a method never returns null. Now with unit tests, you have to write a bunch of test cases where you call the method with varying inputs and verify that the output is not null. Trouble is, you can't test all possible inputs.
With code contracts, you just declare that the method never returns null. The static analyzer will then complain if it is not possible to prove that. If it doesn't complain, you know that your assertion is correct for all possible inputs.
Less work, perfect correctness guarantees. What's not to like?
Contracts allow you say what the actual purpose of the code is, as opposed to letting whatever the code does with whatever random arguments are handed it standing as the definition from the point of view of the compiler, or the next reader of the code. This allows significantly better static analysis and code optimization.
For instance, if I declare an integer parameter (using the contract notation) to be in the range of 1 to 10, and I have a local array in my function declared the same size, that is indexed by the parameter, the compiler can tell that there is no possibility of subscript error, thus producing better code.
You can state that null is valid value in a contract.
The purpose of unit testing is to verify dynamically that the code achieves whatever stated purpose it has. Just because you've written a contract for a function, doesn't mean the code does that, or that static analysis can verify the code does that. Unit testing won't go away.
Well it will not interfere with unit-testing in general. But as I saw you mentioned something about TDD.
If I think about it from that perspective I guess it could/may change the procedure from the standard one
create method (just signature)
create Unit test -> implement the test
run the test: let it fail
implement the method, hack it to the end just to make it working
run the test: see it pass
refactor your (possibly messy) method body
(re-run the test just to see you've not broken anything)
This would be the really hard-full-featured unit-testing procedure. In such a context I guess you could insert code contracts between the 1st and 2nd point like
create method (just signature)
insert code contracts for the methods input parameters
create Unit test -> implement the test
...
The advantage I see at the moment is that you can write easier unit tests in the sense that you wouldn't have to check every possible path since some is already taken into account by your defined contracts. It just gives you additional checking, but it wouldn't replace unit testing since there will always be more logic within the code, more path that have to be tested with unit tests as usual.
Edit
Another possibility I didn't consider before would be to add the code contracts in the refactoring part. Basically as additional way of assuring things. But that would somehow be redundant and since people don't like to do redundant stuff...
Related
Consider unit testing a dictionary object. The first unit tests you might write are a few that simply adds items to the dictionary and check exceptions. The next test may be something like testing that the count is accurate, or that the dictionary returns a correct list of keys or values.
However, each of these later cases requires that the dictionary can first reliably add items. If the tests which add items fail, we have no idea whether our later tests fail because of what they're testing is implemented incorrectly, or because the assumption that we can reliably add items is incorrect.
Can I declare a set of unit tests which cause a given unit test to be inconclusive if any of them fail? If not, how should I best work around this? Have I set up my unit tests wrong, that I'm running into this predicament?
It's not as hard as it might seem. Let's rephrase the question a bit:
If I test my piece of code which requires System.Collections.Generic.List<T>.Add to work, what should I do when one day Microsoft decides to break .Add on List<T>? Do I make my tests depending on this to work inconclusive?
Answer to the above is obvious; you don't. You let them fail for one simple reason - your assumptions have failed, and test should fail. It's the same here. Once you get your add tests to work, from that point on you assume add works. You shouldn't treat your tested code any differently than 3rd party tested code. Once it's proven to work, you assume it indeed does.
On a different note, you can utilize concept called guard assertions. In your remove test, after the arrange phase you introduce additional assert phase, which verifies your initial assumptions (in this case - that the add is working). More information about this technique can be found here.
To add an example, NUnit uses the concept above disguised under the name Theory. This does exactly what you proposed (yet it seems to be more related to data driven testing rather than general utility):
The theory itself is responsible for ensuring that all data supplied meets its assumptions. It does this by use of the Assume.That(...) construct, which works just like Assert.That(...) but does not cause a failure. If the assumption is not satisfied for a particular test case, that case returns an Inconclusive result, rather than a Success or Failure.
However, I think what Mark Seemann states in an answer to the question I linked makes the most sense:
There may be many preconditions that need to be satisfied for a given test case, so you may need more than one Guard Assertion. Instead of repeating those in all tests, having one (and one only) test for each precondition keeps your test code more mantainable, since you will have less repetition that way.
Nice question, I often ponder this and had this problem the other day. What I did was get the basics of our collection working using a dictionary behind the scenes. For example:
public class MyCollection
{
private IDictionary<string, int> backingStore;
public MyCollection(IDictionary<string, int> backingStore)
{
_backingStore = backingStore;
}
}
Then we test drove the addition implementation. As we had the dictionary by reference we could assert that after adding items our business logic was correct.
For example the pseudo code for the additon was something like:
public void Add(Item item)
{
// Check we have not added before
// More business logic...
// Add
}
Then the test could be written such as:
var subject = new MyCollection(backingStore);
subject.Add(new Item())
Assert.That(backingStore.Contains(itemThatWeAdded)
We then went on to drive out the other methods such as retrieval, and deletion.
Your question is what should you do with regards the addition breaking, in turn breaking the retrieval. This is a catch 22 scenario. Personally I'd rather ditch the backing store and use this as an implementation detail. So this is what we did. We refactored the tests to use the system under test, rather than the backing store for the asserts. The great thing about the backing store being public initially is it allows you test drive small parts of the codebase, rather than having to implement both addition and retrieval in one go.
The test for addition then looked like the following after we refactored the collection to not expose the backing store.
var subject = new MyCollection();
var item = new Item()
subject.Add(item)
Assert.That(subject.Has(item), Is.True);
In this case I think this is fine. If you can not add items successfully then you sure as hell cannot retrieve anything because you've not added them. As long as your tests are named well any developer seeing some test such as "CanOnlyAddUniqueItemsToCollection" will point future developers in the right direction, in other words, the addition is broken. Just make sure your tests are named well and you should be giving as much help as possible.
I don't see this as too much of a problem. If your Dictionary class is not too big, and the unit test for that class is the only unit test testing that code, then when your add method is broken and multiple tests fail, you still know the problem is in the Dictionary class and can identify it, debug and fix it easily.
Where it becomes a problem is when you have other code smells or design problems such as:
unit tests tests are testing many application classes, using mocks instead can help here.
unit tests are actually system tests creating and testing many application classes at once.
the Dictionary class is too big and complex so when it breaks and tests fail it's difficult to figure out what part is broken.
This is very interesting. We use NUnit and the best I can tell it runs test-methods alphabetically. That might be an overly-artificial way to order your tests, but if you built up your test classes such that alphabetically/numerically-named pre-req methods came first you might accomplish what you want.
I find myself writing a test method, firing just it to watch it fail, and then writing the code to make it pass. When I'm all done I can run the whole class and everything passes - it doesn't matter what order the tests ran in becuase everything 'works' becuase of the incremental dev I did.
Now later on if I break something in the thing i'm testing who knows what all will fail in the harness. I guess it doesn't really matter to me - I've got a long list of failures and I can tease out what went wrong.
I have a method that checks some assumptions and either follows the happy path, or terminates along the unhappy paths. I've either designed it poorly, or I'm missing the method for testing that the control of the flow.
if (this.officeInfo.OfficeClosed)
{
this.phoneCall.InformCallerThatOfficeIsClosedAndHangUp();
return;
}
if (!this.operators.GetAllOperators().Any())
{
this.phoneCall.InformCallerThatNoOneIsAvailableAndSendToVoicemail();
return;
}
Call call=null;
forach(var operator in this.operators.GetAllOperators())
{
call = operator.Call();
if(call!=null) {break;}
}
and so on. I've got my dependencies injected. I've got my mocks moq'd. I can make sure that this or that is called, but I don't know how to test that the "return" happens. If TDD means I don't write a line until I have a test that fails without it, I'm stuck.
How would you test it? Or is there a way to write it that makes it more testable?
Update: Several answers have been posted saying that I should test the resultant calls, not the flow control. The problem I have with this approach, is that every test is required to setup and test the state and results of the other tests. This seems really unwieldy and brittle. Shouldn't I be able to test the first if clause alone, and then test the second one alone? Do I really need to have a logarithmically expanding set of tests that start looking like Method_WithParameter_DoesntInvokeMethod8IfMethod7IsTrueandMethod6IsTrueAndMethod5IsTrueAndMethod4IsTrueAndMethod3IsFalseAndMethod2IsTrueAndMethod1isAaaaccck()?
I think you want to test the program's outputs: for example, that when this.officeInfo.OfficeClosed then the program does invoke this.phoneCall.InformCallerThatOfficeIsClosedAndHangUp() and does not invoke other methods such as this.operators.GetAllOperators().
I think that your test does this by asking its mock objects (phoneCall, etc.) which of their methods was invoked, or by getting them to throw an exception if any of their methods are invoked unexpectedly.
One way to do it is to make a log file of the program's inputs (e.g. 'OfficeClosed returns true') and outputs: then run the test, let the test generate the log file, and then assert that the contents of the generated log file match the expected log file contents for that test.
I'm not sure that's really the right approach. You care about whether or not the method produced the expected result, not necessarily how control "flowed" through the particular method. For example, if phoneCall.InformCallerThatOfficeIsClosedAndHangUp is called, then I assume some result is recorded somewhere. So in your unit test, you would be asserting that result was indeed recorded (either by checking a database record, file, etc.).
With that said, it's important to ensure that your unit tests indeed cover your code. For that, you can use a tool like NCover to ensure that all of your code is being excercised. It'll generate a coverage report which will show you exactly which lines were executed by your unit tests and more importantly, which ones weren't.
You could go ballistic and use a strategy pattern. Something along the lines of having an interface IHandleCall, with a single void method DoTheRightThing(), and 3 classes HandleOfficeIsClosed, HandleEveryoneIsBusy, HandleGiveFirstOperatorAvailable, which implement the interface. And then have code like:
IHandleCall handleCall;
if (this.officeInfo.OfficeClosed)
{
handleCall = new HandleOfficeIsClosed();
}
else if other condition
{
handleCall = new OtherImplementation();
}
handleCall.DoTheRightThing();
return;
That way you can get rid of the multiple return points in your method. Note that this is a very dirty outline, but essentially at that point you should extract the if/else into some factory, and then the only thing you have to test is that your class calls the factory, and that handleCall.DoTheRightThing() is called - (and of course that the factory returns the right strategy).
In any case, because you have already guarded against no operator available, you could simplify the end to:
var operator = this.operators.FindFirst();
call = operator.Call();
Don't test the flow control, just test the expected behavior. That is, unit testing does not care about the implementation details, only that the behavior of the method matches the specifications of the method. So if Add(int x, int y) should produce the result 4 on input x = 2, y = 2, then test that the output is 4 but don't worry about how Add produced the result.
To put it another way, unit testing should be invariant under implementation details and refactoring. But if you're testing implementation details in your unit testing, then you can't refactor without breaking the unit tests. For example, if you implement a method GetPrime(int k) to return the kth prime then check that GetPrime(10) returns 29 but don't test the flow control inside the method. If you implement GetPrime using the Sieve of Eratóstenes and have tested the flow control inside the method and later refactor to use the Sieve of Atkin, your unit tests will break. Again, all that matters is that GetPrime(10) returns 29, not how it does it.
If you are stuck using TDD it's a good thing: it means that TDD drives your design and you are looking into how to change it so you can test it.
You can either:
1) verify state: check SUT state after SUT execution or
2) verify behavior: check that mock object calls complied with test expectations
If you don't like how either of these approaches look in your test it's time to refactor the code.
The pattern described by Aaron Feng and K. Scott Allen would solve for my problem and it's testability concerns. The only issue I see is that it requires all the computation to be performed up front. The decision data object needs to be populated before all of the conditionals. That's great unless it requires successive round trips to the persistence storage.
If a function just calls another function or performs actions. How do I test it? Currently, I enforce all the functions should return a value so that I could assert the function return values. However, I think this approach mass up the API because in the production code. I don't need those functions to return value. Any good solutions?
I think mock object might be a possible solution. I want to know when should I use assert and when should I use mock objects? Is there any general guide line?
Thank you
Let's use BufferedStream.Flush() as an example method that doesn't return anything; how would we test this method if we had written it ourselves?
There is always some observable effect, otherwise the method would not exist. So the answer can be to test for the effect:
[Test]
public void FlushWritesToUnderlyingStream()
{
var memory = new byte[10];
var memoryStream = new MemoryStream(memory);
var buffered = new BufferedStream(memoryStream);
buffered.Write(0xFF);
Assert.AreEqual(0x00, memory[0]); // not yet flushed, memory unchanged
buffered.Flush();
Assert.AreEqual(0xFF, memory[0]); // now it has changed
}
The trick is to structure your code so that these effects aren't too hard to observe in a test:
explicitly pass collaborator objects,
just like how the memoryStream is passed
to the BufferedStream in the constructor.
This is called dependency
injection.
program against an interface, just
like how BufferedStream is programmed
against the Stream interface. This enables
you to pass simpler, test-friendly implementations (like MemoryStream in this case) or use a mocking framework (like MoQ or RhinoMocks), which is all great for unit testing.
Sorry for not answering straight but ... are you sure you have the exact balance in your testing?
I wonder if you are not testing too much ?
Do you really need to test a function that merely delegates to another?
Returns only for the tests
I agree with you when you write you don't want to add return values that are useful only for the tests, not for production. This clutters your API, making it less clear, which is a huge cost in the end.
Also, your return value could seem correct to the test, but nothing says that the implementation is returning the return value that corresponds to the implementation, so the test is probably not proving anything anyway...
Costs
Note that testing has an initial cost, the cost of writing the test.
If the implementation is very easy, the risk of failure is ridiculously low, but the time spend testing still accumulates (over hundred or thousands cases, it ends up being pretty serious).
But more than that, each time you refactor your production code, you will probably have to refactor your tests also. So the maintenance cost of your tests will be high.
Testing the implementation
Testing what a method does (what other methods it calls, etc) is critized, just like testing a private method... There are several points made:
this is fragile and costly : any code refactoring will break the tests, so this increases the maintenance cost
Testing a private method does not bring much safety to your production code, because your production code is not making that call. It's like verifying something you won't actually need.
When a code delegates effectively to another, the implementation is so simple that the risk of mistakes is very low, and the code almost never changes, so what works once (when you write it) will never break...
Yes, mock is generally the way to go, if you want to test that a certain function is called and that certain parameters are passed in.
Here's how to do it in Typemock (C#):
Isolate.Verify.WasCalledWithAnyArguments(()=> myInstance.WeatherService("","", null,0));
Isolate.Verify.WasCalledWithExactArguments(()=> myInstance. StockQuote("","", null,0));
In general, you should use Assert as much as possible, until when you can't have it ( For example, when you have to test whether you call an external Web service API properly, in this case you can't/ don't want to communicate with the web service directly). In this case you use mock to verify that a certain web service method is correctly called with correct parameters.
"I want to know when should I use assert and when should I use mock objects? Is there any general guide line?"
There's an absolute, fixed and important rule.
Your tests must contain assert. The presence of assert is what you use to see if the test passed or failed. A test is a method that calls the "component under test" (a function, an object, whatever) in a specific fixture, and makes specific assertions about the component's behavior.
A test asserts something about the component being tested. Every test must have an assert, or it isn't a test. If it doesn't have assert, it's not clear what you're doing.
A mock is a replacement for a component to simplify the test configuration. It is a "mock" or "imitation" or "false" component that replaces a real component. You use mocks to replace something and simplify your testing.
Let's say you're going to test function a. And function a calls function b.
The tests for function a must have an assert (or it's not a test).
The tests for a may need a mock for function b. To isolate the two functions, you test a with a mock for function b.
The tests for function b must have an assert (or it's not a test).
The tests for b may not need anything mocked. Or, perhaps b makes an OS API call. This may need to be mocked. Or perhaps b writes to a file. This may need to be mocked.
Given the following SUT, would you consider this unit test to be unnecessary?
**edit : we cannot assume the names will match, so reflection wouldn't work.
**edit 2 : in actuality, this class would implement an IMapper interface and there would be full blown behavioral (mock) testing at the business logic layer of the application. this test just happens to be the lowest level of testing that must be state based. I question whether this test is truly necessary because the test code is almost identical to the source code itself, and based off of actual experience I don't see how this unit test makes maintenance of the application any easier.
//SUT
public class Mapper
{
public void Map(DataContract from, DataObject to)
{
to.Value1 = from.Value1;
to.Value2 = from.Value2;
....
to.Value100 = from.Value100;
}
}
//Unit Test
public class MapperTest()
{
DataContract contract = new DataContract(){... } ;
DataObject do = new DataObject(){...};
Mapper mapper = new Mapper();
mapper.Map(contract, do);
Assert.AreEqual(do.Value1, contract.Value1);
...
Assert.AreEqual(do.Value100, contract.Value100);
}
i would question the construct itself, not the need to test it
[reflection would be far less code]
I'd argue that it is necessary.
However, it would be better as 100 separate unit tests, each that check one value.
That way, when you something go wrong with value65, you can run the tests, and immediately find that value65 and value66 are being transposed.
Really, it's this kind of simple code where you switch your brain off and forget about that errors happen. Having tests in place means you pick them up and not your customers.
However, if you have a class with 100 properties all named ValueXXX, then you might be better using an Array or a List.
It is not excessive. I'm sure not sure it fully focuses on what you want to test.
"Under the strict definition, for QA purposes, the failure of a UnitTest implicates only one unit. You know exactly where to search to find the bug."
The power of a unit test is in having a known correct resultant state, the focus should be the values assigned to DataContract. Those are the bounds we want to push. To ensure that all possible values for DataContract can be successfully copied into DataObject. DataContract must be populated with edge case values.
PS. David Kemp is right 100 well designed tests would be the most true to the concept of unit testing.
Note : For this test we must assume that DataContract populates perfectly when built (that requires separate tests).
It would be better if you could test at a higher level, i.e. the business logic that requires you to create the Mapper.Map() function.
Not if this was the only unit test of this kind in the entire app. However, the second another like it showed up, you'd see me scrunch my eyebrows and start thinking about reflection.
Not Excesive.
I agree the code looks strange but that said:
The beauty of unit test is that once is done is there forever, so if anyone for any reason decides to change that implementation for something more "clever" still the test should pass, so not a big deal.
I personally would probably have a perl script to generate the code as I would get bored of replacing the numbers for each assert, and I would probably make some mistakes on the way, and the perl script (or what ever script) would be faster for me.
What Makes a Good Unit Test? says that a test should test only one thing. What is the benefit from that?
Wouldn't it be better to write a bit bigger tests that test bigger block of code? Investigating a test failure is anyway hard and I don't see help to it from smaller tests.
Edit: The word unit is not that important. Let's say I consider the unit a bit bigger. That is not the issue here. The real question is why make a test or more for all methods as few tests that cover many methods is simpler.
An example: A list class. Why should I make separate tests for addition and removal? A one test that first adds then removes sounds simpler.
Testing only one thing will isolate that one thing and prove whether or not it works. That is the idea with unit testing. Nothing wrong with tests that test more than one thing, but that is generally referred to as integration testing. They both have merits, based on context.
To use an example, if your bedside lamp doesn't turn on, and you replace the bulb and switch the extension cord, you don't know which change fixed the issue. Should have done unit testing, and separated your concerns to isolate the problem.
Update: I read this article and linked articles and I gotta say, I'm shook: https://techbeacon.com/app-dev-testing/no-1-unit-testing-best-practice-stop-doing-it
There is substance here and it gets the mental juices flowing. But I reckon that it jibes with the original sentiment that we should be doing the test that context demands. I suppose I'd just append that to say that we need to get closer to knowing for sure the benefits of different testing on a system and less of a cross-your-fingers approach. Measurements/quantifications and all that good stuff.
I'm going to go out on a limb here, and say that the "only test one thing" advice isn't as actually helpful as it's sometimes made out to be.
Sometimes tests take a certain amount of setting up. Sometimes they may even take a certain amount of time to set up (in the real world). Often you can test two actions in one go.
Pro: only have all that setup occur once. Your tests after the first action will prove that the world is how you expect it to be before the second action. Less code, faster test run.
Con: if either action fails, you'll get the same result: the same test will fail. You'll have less information about where the problem is than if you only had a single action in each of two tests.
In reality, I find that the "con" here isn't much of a problem. The stack trace often narrows things down very quickly, and I'm going to make sure I fix the code anyway.
A slightly different "con" here is that it breaks the "write a new test, make it pass, refactor" cycle. I view that as an ideal cycle, but one which doesn't always mirror reality. Sometimes it's simply more pragmatic to add an extra action and check (or possibly just another check to an existing action) in a current test than to create a new one.
Tests that check for more than one thing aren't usually recommended because they are more tightly coupled and brittle. If you change something in the code, it'll take longer to change the test, since there are more things to account for.
[Edit:]
Ok, say this is a sample test method:
[TestMethod]
public void TestSomething() {
// Test condition A
// Test condition B
// Test condition C
// Test condition D
}
If your test for condition A fails, then B, C, and D will appear to fail as well, and won't provide you with any usefulness. What if your code change would have caused C to fail as well? If you had split them out into 4 separate tests, you would know this.
Haaa... unit tests.
Push any "directives" too far and it rapidly becomes unusable.
Single unit test test a single thing is just as good practice as single method does a single task. But IMHO that does not mean a single test can only contain a single assert statement.
Is
#Test
public void checkNullInputFirstArgument(){...}
#Test
public void checkNullInputSecondArgument(){...}
#Test
public void checkOverInputFirstArgument(){...}
...
better than
#Test
public void testLimitConditions(){...}
is question of taste in my opinion rather than good practice. I personally much prefer the latter.
But
#Test
public void doesWork(){...}
is actually what the "directive" wants you to avoid at all cost and what drains my sanity the fastest.
As a final conclusion, group together things that are semantically related and easilly testable together so that a failed test message, by itself, is actually meaningful enough for you to go directly to the code.
Rule of thumb here on a failed test report: if you have to read the test's code first then your test are not structured well enough and need more splitting into smaller tests.
My 2 cents.
Think of building a car. If you were to apply your theory, of just testing big things, then why not make a test to drive the car through a desert. It breaks down. Ok, so tell me what caused the problem. You can't. That's a scenario test.
A functional test may be to turn on the engine. It fails. But that could be because of a number of reasons. You still couldn't tell me exactly what caused the problem. We're getting closer though.
A unit test is more specific, and will firstly identify where the code is broken, but it will also (if doing proper TDD) help architect your code into clear, modular chunks.
Someone mentioned about using the stack trace. Forget it. That's a second resort. Going through the stack trace, or using debug is a pain and can be time consuming. Especially on larger systems, and complex bugs.
Good characteristics of a unit test:
Fast (milliseconds)
Independent. It's not affected by or dependent on other tests
Clear. It shouldn't be bloated, or contain a huge amount of setup.
Using test-driven development, you would write your tests first, then write the code to pass the test. If your tests are focused, this makes writing the code to pass the test easier.
For example, I might have a method that takes a parameter. One of the things I might think of first is, what should happen if the parameter is null? It should throw a ArgumentNull exception (I think). So I write a test that checks to see if that exception is thrown when I pass a null argument. Run the test. Okay, it throws NotImplementedException. I go and fix that by changing the code to throw an ArgumentNull exception. Run my test it passes. Then I think, what happens if it's too small or too big? Ah, that's two tests. I write the too small case first.
The point is I don't think of the behavior of the method all at once. I build it incrementally (and logically) by thinking about what it should do, then implement code and refactoring as I go to make it look pretty (elegant). This is why tests should be small and focused because when you are thinking about the behavior you should develop in small, understandable increments.
Having tests that verify only one thing makes troubleshooting easier. It's not to say you shouldn't also have tests that do test multiple things, or multiple tests that share the same setup/teardown.
Here should be an illustrative example. Let's say that you have a stack class with queries:
getSize
isEmpty
getTop
and methods to mutate the stack
push(anObject)
pop()
Now, consider the following test case for it (I'm using Python like pseudo-code for this example.)
class TestCase():
def setup():
self.stack = new Stack()
def test():
stack.push(1)
stack.push(2)
stack.pop()
assert stack.top() == 1, "top() isn't showing correct object"
assert stack.getSize() == 1, "getSize() call failed"
From this test case, you can determine if something is wrong, but not whether it is isolated to the push() or pop() implementations, or the queries that return values: top() and getSize().
If we add individual test cases for each method and its behavior, things become much easier to diagnose. Also, by doing fresh setup for each test case, we can guarantee that the problem is completely within the methods that the failing test method called.
def test_size():
assert stack.getSize() == 0
assert stack.isEmpty()
def test_push():
self.stack.push(1)
assert stack.top() == 1, "top returns wrong object after push"
assert stack.getSize() == 1, "getSize wrong after push"
def test_pop():
stack.push(1)
stack.pop()
assert stack.getSize() == 0, "getSize wrong after push"
As far as test-driven development is concerned. I personally write larger "functional tests" that end up testing multiple methods at first, and then create unit tests as I start to implement individual pieces.
Another way to look at it is unit tests verify the contract of each individual method, while larger tests verify the contract that the objects and the system as a whole must follow.
I'm still using three method calls in test_push, however both top() and getSize() are queries that are tested by separate test methods.
You could get similar functionality by adding more asserts to the single test, but then later assertion failures would be hidden.
If you are testing more than one thing then it is called an Integration test...not a unit test. You would still run these integration tests in the same testing framework as your unit tests.
Integration tests are generally slower, unit tests are fast because all dependencies are mocked/faked, so no database/web service/slow service calls.
We run our unit tests on commit to source control, and our integration tests only get run in the nightly build.
If you test more than one thing and the first thing you test fails, you will not know if the subsequent things you are testing pass or fail. It is easier to fix when you know everything that will fail.
Smaller unit test make it more clear where the issue is when they fail.
The GLib, but hopefully still useful, answer is that unit = one. If you test more than one thing, then you aren't unit testing.
Regarding your example: If you are testing add and remove in the same unit test, how do you verify that the item was ever added to your list? That is why you need to add and verify that it was added in one test.
Or to use the lamp example: If you want to test your lamp and all you do is turn the switch on and then off, how do you know the lamp ever turned on? You must take the step in between to look at the lamp and verify that it is on. Then you can turn it off and verify that it turned off.
I support the idea that unit tests should only test one thing. I also stray from it quite a bit. Today I had a test where expensive setup seemed to be forcing me to make more than one assertion per test.
namespace Tests.Integration
{
[TestFixture]
public class FeeMessageTest
{
[Test]
public void ShouldHaveCorrectValues
{
var fees = CallSlowRunningFeeService();
Assert.AreEqual(6.50m, fees.ConvenienceFee);
Assert.AreEqual(2.95m, fees.CreditCardFee);
Assert.AreEqual(59.95m, fees.ChangeFee);
}
}
}
At the same time, I really wanted to see all my assertions that failed, not just the first one. I was expecting them all to fail, and I needed to know what amounts I was really getting back. But, a standard [SetUp] with each test divided would cause 3 calls to the slow service. Suddenly I remembered an article suggesting that using "unconventional" test constructs is where half the benefit of unit testing is hidden. (I think it was a Jeremy Miller post, but can't find it now.) Suddenly [TestFixtureSetUp] popped to mind, and I realized I could make a single service call but still have separate, expressive test methods.
namespace Tests.Integration
{
[TestFixture]
public class FeeMessageTest
{
Fees fees;
[TestFixtureSetUp]
public void FetchFeesMessageFromService()
{
fees = CallSlowRunningFeeService();
}
[Test]
public void ShouldHaveCorrectConvenienceFee()
{
Assert.AreEqual(6.50m, fees.ConvenienceFee);
}
[Test]
public void ShouldHaveCorrectCreditCardFee()
{
Assert.AreEqual(2.95m, fees.CreditCardFee);
}
[Test]
public void ShouldHaveCorrectChangeFee()
{
Assert.AreEqual(59.95m, fees.ChangeFee);
}
}
}
There is more code in this test, but it provides much more value by showing me all the values that don't match expectations at once.
A colleague also pointed out that this is a bit like Scott Bellware's specunit.net: http://code.google.com/p/specunit-net/
Another practical disadvantage of very granular unit testing is that it breaks the DRY principle. I have worked on projects where the rule was that each public method of a class had to have a unit test (a [TestMethod]). Obviously this added some overhead every time you created a public method but the real problem was that it added some "friction" to refactoring.
It's similar to method level documentation, it's nice to have but it's another thing that has to be maintained and it makes changing a method signature or name a little more cumbersome and slows down "floss refactoring" (as described in "Refactoring Tools: Fitness for Purpose" by Emerson Murphy-Hill and Andrew P. Black. PDF, 1.3 MB).
Like most things in design, there is a trade-off that the phrase "a test should test only one thing" doesn't capture.
When a test fails, there are three options:
The implementation is broken and should be fixed.
The test is broken and should be fixed.
The test is not anymore needed and should be removed.
Fine-grained tests with descriptive names help the reader to know why the test was written, which in turn makes it easier to know which of the above options to choose. The name of the test should describe the behaviour which is being specified by the test - and only one behaviour per test - so that just by reading the names of the tests the reader will know what the system does. See this article for more information.
On the other hand, if one test is doing lots of different things and it has a non-descriptive name (such as tests named after methods in the implementation), then it will be very hard to find out the motivation behind the test, and it will be hard to know when and how to change the test.
Here is what a it can look like (with GoSpec), when each test tests only one thing:
func StackSpec(c gospec.Context) {
stack := NewStack()
c.Specify("An empty stack", func() {
c.Specify("is empty", func() {
c.Then(stack).Should.Be(stack.Empty())
})
c.Specify("After a push, the stack is no longer empty", func() {
stack.Push("foo")
c.Then(stack).ShouldNot.Be(stack.Empty())
})
})
c.Specify("When objects have been pushed onto a stack", func() {
stack.Push("one")
stack.Push("two")
c.Specify("the object pushed last is popped first", func() {
x := stack.Pop()
c.Then(x).Should.Equal("two")
})
c.Specify("the object pushed first is popped last", func() {
stack.Pop()
x := stack.Pop()
c.Then(x).Should.Equal("one")
})
c.Specify("After popping all objects, the stack is empty", func() {
stack.Pop()
stack.Pop()
c.Then(stack).Should.Be(stack.Empty())
})
})
}
The real question is why make a test or more for all methods as few tests that cover many methods is simpler.
Well, so that when some test fails you know which method fails.
When you have to repair a non-functioning car, it is easier when you know which part of the engine is failing.
An example: A list class. Why should I make separate tests for addition and removal? A one test that first adds then removes sounds simpler.
Let's suppose that the addition method is broken and does not add, and that the removal method is broken and does not remove. Your test would check that the list, after addition and removal, has the same size as initially. Your test would be in success. Although both of your methods would be broken.
Disclaimer: This is an answer highly influenced by the book "xUnit Test Patterns".
Testing only one thing at each test is one of the most basic principles that provides the following benefits:
Defect Localization: If a test fails, you immediately know why it failed (ideally without further troubleshooting, if you've done a good job with the assertions used).
Test as a specification: the tests are not only there as a safety net, but can easily be used as specification/documentation. For instance, a developer should be able to read the unit tests of a single component and understand the API/contract of it, without needing to read the implementation (leveraging the benefit of encapsulation).
Infeasibility of TDD: TDD is based on having small-sized chunks of functionality and completing progressive iterations of (write failing test, write code, verify test succeeds). This process get highly disrupted if a test has to verify multiple things.
Lack of side-effects: Somewhat related to the first one, but when a test verifies multiple things, it's more possible that it will be tied to other tests as well. So, these tests might need to have a shared test fixture, which means that one will be affected by the other one. So, eventually you might have a test failing, but in reality another test is the one that caused the failure, e.g. by changing the fixture data.
I can only see a single reason why you might benefit from having a test that verifies multiple things, but this should be seen as a code smell actually:
Performance optimisation: There are some cases, where your tests are not running only in memory, but are also dependent in persistent storage (e.g. databases). In some of these cases, having a test verify multiple things might help in decreasing the number of disk accesses, thus decreasing the execution time. However, unit tests should ideally be executable only in memory, so if you stumble upon such a case, you should re-consider whether you are going in the wrong path. All persistent dependencies should be replaced with mock objects in unit tests. End-to-end functionality should be covered by a different suite of integration tests. In this way, you do not need to care about execution time anymore, since integration tests are usually executed by build pipelines and not by developers, so a slightly higher execution time has almost no impact to the efficiency of the software development lifecycle.