Showing posts with label testing. Show all posts
Showing posts with label testing. Show all posts

Thursday, September 3, 2009

On abstracting out static contexts

Any test favoring developer will tell you singletons are the bane of testability. Static contexts make testing difficult. When dealing with legacy code, we are often faced with the problem of testing around such problematic coupling. I've had enough opportunity to dable in such code to have developed some strategies in dealing with coupling. In this post I'll present a strategy I refer to as abstracting out singletons.

For the purpose of discussion I present the following code fragment:

public class TightlyCoupled {
 public void quickPurchase(String customerReferenceNumber, 
    int productCode, int quantity) {
  CustomerCatalog customers = CustomerCatalog.getInstance();
  Customer purchaser = customers.find(customerReferenceNumber);
  if (purchaser == null)
   throw new UnknownCustomerException(customerReferenceNumber);
  OrderItem requestedItems = Inventory.getInstance().take(productCode, quantity);
  Order quickOrder = new Order(purchaser);
  quickOrder.add(requestedItems);
  // ...
 }
}

This code is displays the symptoms of coupling that clearly make testing difficult. Can the inventory and customer catalog be easly programmed to behave in a predictable and testable manner? Your answer will be contextual, but often they cannot. In most enterprise application such singletons will bootstrap the initialization of other singletons as well as database connections. Can these services be easily mocked? Doubtful.

I have seen cases where programmers will allow themselves to override the singleton before the test scenario. This works, but does not help to move away from the current programming model.

What we want is to be able to provide an alternate implementation of these services to a constructed instance of the class under test (CUT). The problem with using a dependency injection approach to testing is that most applications developed with singleton spread are rarely blessed with an IoC container to facilitate object construction. Consequently we can rarely allow ourselves to remove the default constructor of the CUT.

So the first thing that I propose is that we consider using a DI approach, and in order to support this we provide two constructors for our CUT: one that allows for the injection of the dependencies, an a default constructor that wires the class with the default dependencies. This quickly leads us to something like this:

public class LessCoupledThroughConstructor {
 private CustomerCatalog customers;
 private Inventory inventory;

 public LessCoupledThroughConstructor(
   CustomerCatalog customers,
   Inventory inventory) {
  this.customers = customers;
  this.inventory = inventory;
 }

 public LessCoupledThroughConstructor() {
  this(CustomerCatalog.getInstance(), Inventory.getInstance());
 }

 public void quickPurchase(
  String customerReferenceNumber, 
  int productCode, int quantity) {
  Customer purchaser = customers.find(customerReferenceNumber);
  if (purchaser == null)
   throw new UnknownCustomerException(customerReferenceNumber);
  OrderItem requestedItems = inventory.take(productCode, quantity);
  Order quickOrder = new Order(purchaser);
  quickOrder.add(requestedItems);
  // ...
 }
}

So now our CUT can be passed a test friendly set of dependencies with a minimal impact on existing code. What's more, if the CUT is normally treated as a singleton also, its default constructor can become private, and its instance accessor would not take any dependencies. The test friendly constructor would remain public.

This approach has one drawback. When working on a legacy system that relied heavily on singleton accessors to initialize its services, I quickly discovered that many of these services had developed a kind of magical initialization order. No one knew for certain what that order was, but many bugs in the system were to it. This minor adjustment I proposed broke the initialization order: the dependency services where initialized in the constructor, and not on first use in the using method. Ka-boom!

No problem. The solution is to introduce a minimal amount of abstraction. I decided to creation some wrapper services. These services delegated operations to the static context on a per-call basis, but where not singletons or static instances themselves. This produces the code bellow:

public class LessCoupledThroughWraping {
 private CustomerCatalog customers;
 private Inventory inventory;
 
 public LessCoupledThroughWraping(
   CustomerCatalogService customers,
   InventoryService inventory) {
  this.customers = customers;
  this.inventory = inventory;
 }

 public LessCoupledThroughWraping() {
  this(new GlobalCustomerCatalogService(), new GlobalInventoryService());
 }

 public void quickPurchase(
   String customerReferenceNumber, 
   int productCode, int quantity) {
  // as previous example
 }
}

To give you an idea of what the wraper services look like:

public class GlobalInventoryService implements InventoryService {
 public OrderItem take(int productCode, int quantity) {
  return Inventory.getInstance().take(productCode, quantity);
 }
}

This approached allows the CUT to define a default set of dependencies, but still provides the possibility to inject dependencies.

These approaches are clearly not good examples of applied TDD, but on large systems that have a great deal of code coupling it is often necessary to introduce such test seams. Gradually a large system becomes split into an IoC container friendly design with well tested components.

Other areas where such a strategies work include all static data, static third party APIs, as well as static platform functinality.

An example that I used recently involves the .NET runtime's “DateTime.Now” which can make testing time dependent behavior unpredictable. My goal was to test that after 10 minutes some condition expired. Obviously I didn't want a test that ran for ten minutes. I abstract out a date and time service and at test time I substitute a date time service on which I can set the desired date and time. This makes testing cleaner and more reliable, and in this last case I only needed to redefine the date/time service's time to jump ahead ten minutes.

What do you think? Have you encountered similar problems? What did you do to work around them?

Friday, February 13, 2009

On testing data objects, applied TDD

In a previous post I discussed how you might approach unit testing a data object. In a test-first approach to development, we first write the tests (obviously), and then write the simplest code that makes the test pass. In this exercise I will propose the tests, writing the code will be up to you. This exercise is designed to be written in Java with JUnit 3, but can be easily adapted to another xUnit framework and language.

The context of the exercise is the reservation of tables at a fair. A person can reserve one or more tables on which to present their offerings.

First lets imagine we want to create a reservation for a single table by 'bob'.

public void testSingleTableReservationBelongsToReserver() {
 Reservation bobsReservation = Reservation.forSingleTable("bob");
 assertEquals("bob", bobsReservation.getReservedBy());
}

Ask yourself what the simplest code to implement the reservation class would look like, and write it. Make sure your test passes.

Next we will validate that the reservation is specifically for one table. This is another independent test since I want to be able to easily identify the source of failure if a test fails. Chaining multiple assertions in a single test can cause one failure to hide another.

public void testSingleTableReservationReservesOneTable() {
 Reservation bobsReservation = Reservation.forSingleTable("bob");
 assertEquals(1, bobsReservation.getNumberOfTables());
}

Again, implement the simplest code to make the test pass. In this case the method getNumberOfTables could return the constant '1'.

Next let's validate that the cost of the reservation is the sum of the registration fee (30$) and the cost per table (20$). Again this method could return a hard-coded constant.

public void testCostOfSingleTableReservationIsCostPerTablePlusRegistrationFee();
 Reservation bobsReservation = Reservation.forSingleTable("bob");
 assertEquals(50, bobsReservation.getTotalReservationCost());
}

Next let's do a negative test for the creation of a multi-table reservation. The maximum number of tables allowed is 4 per person.

public void testCannotCreateReservationForMoreThanMaxAllowedTables() {
 try {
  Reservation.forMultipleTable("jim", 5);
  fail();
 } catch (TooManyTablesRequestedException a) {
 }
}

It seems that Jim's out of luck. This test forced us to create a new factory method as well as a new exception. If your new method has an if statement, you've already gone too far. At this point your method should only contain a single statement: a throw new exception. I know it's painfuly, small step to many newbies, but we are working our way up slowly as would be done in TDD. Its is an incremental development process.

Ok, maybe Jim can settle for reserving 4 tables.

public void testCreateReservationForMaximumNumberOfTables() throws Exception {
 Reservation joesReservation = Reservation.forMultipleTable("joe", 4);
 assertEquals(4, joesReservation.getNumberOfTables());
}

So far things are moving along well. Our business rules for the calculation of the cost of the reservation may need some elaborating. Lets ask for the cost of reserving two tables.

public void testCostOfReservationForTwo() throws Exception{
  Reservation chrisReservation = Reservation.forMultipleTable("chris", 2);
  assertEquals(70, chrisReservation.getTotalReservationCost());
}

At this point we may apply a 10% discount to Jim's reservation because he's reserving 4 tables.

public void testCostOfReservationForFourTablesIncludesDiscount() throws Exception{
  Reservation joesReservation = Reservation.forMultipleTable("joe", 4);
  assertEquals(99, joesReservation.getTotalReservationCost());
}

We will now add two tests that will target the updating of the data object's numberOfTables property.

public void testChangeNumberOfTablesReservedOverridesPreviousNumber() throws Exception {
  Reservation joesReservation = Reservation.forMultipleTable("joe", 4);
  joesReservation.setNumberOfTables(3);
  assertEquals(3, joesReservation.getNumberOfTables());
}
 
public void testChangeNumberOfTablesReservedChangesCostOfReservation() throws Exception {
  Reservation joesReservation = Reservation.forMultipleTable("joe", 4);
  joesReservation.setNumberOfTables(1);
  assertEquals(50, joesReservation.getTotalReservationCost());
}

In many baby steps we will have developed a simple, well tested implementation of Reservation. This particular exercise is trivial, and did not push us to do any complicated refactoring. In practice we would want to follow the tree step TDD cycle: red, green, refactor.

Usually the hardest part to test-first development is imagining what to test. I hope this exercise has shed some light on how to start the test writing.

Sunday, February 8, 2009

On handling exceptions in happy path tests

I'd like to start this post with a reflection on the goal of unit testing.

The goal of unit tests is to validate the single, precise behavior of a block of code. This indicates that a test's value is directly dependent on its ability to spot deviations in that behavior.

So lets look at a simple test, the likes of which I have seen before:

void testSomeBizareCondition() {
 try {
  op.someOperartionThatShouldBeOK();
  assertEquals("abc", op.getValue());
 } catch (NullPointerException e) {
  fail("oops");
 } catch (SomeOperationException e) {
  fail("oops");
 }
}

What is wrong with this test? If the test should fail with an "oops" there is no way to differentiate between the two error conditions. Instead of reading the test report logs and quickly correcting the code, some developers will find themselves debugging the tests. The answer to that is not to change the failure messages for each catch, because this would lead us down the path of catching every imaginable exception type.

Another reason not to favor catching of individual exception types is illustrated bellow:

void testSomeBizareConditionThrowsAnOperationException() {
 try {
  op.someOperartionThatShouldBeOK();
  fail("oops");
 } catch (NullPointerException e) {
  fail("oops");
 } catch (SomeOperationException e) {
 }
}

At a glance, can you tell what the goal of the test is? Maybe you can, but it isn't obvious, and you shouldn't have to thing about it. This test is intended to validate that SomeOperationException is thrown during test execution. Unfortunately this intention is not evident.

In order to illustrate the solution to both of the above tests, lets look at the basic concept. Bellow is a test that represents a simplified version of the first example.

void testSomeOptimisticCondition() {
 try {
  op.someOperationThatShouldBeOK();
  assertEquals("abc", op.getValue());
 } catch (Trowable th) {
  fail("bad");
 }
}

This example still masks the failure reason, the cause of exception is not in evidence. The stack trace and exception details go a long way to facilitating a quick code correction.

A second problem with this test is that it masks the details of the assertion failure. This could be corrected by changing the catch type from Throwable to Exception. This is because an assertion failure is expressed in the form of an exception.

The solution

Its hard to imagine, but sometimes the best thing to do is let the exception propagate out of the test. For unchecked exceptions this is easy, the test will fail, and the cause will be plain as day in the test output.

For checked exceptions this is a little more complicated, but only slightly. Let the test method throw a generic exception.

void testSomeOptimisticCondition() throws Exception {
 op.someOperationThatShouldBeOK();
 assertEquals("abc", op.getValue());
}

Not only does the test now guaranty that all failure conditions are now explicit, it makes the test a lot clearer.

Test cleanup

Now all this discussion on removing clutter may well have some readers thinking: "yeah, but I use a catch to cleanup after the test". It is true that some tests require the cleaning of external resources. The problem with doing so in a try/catch is that it can complicate test logic. There are two viable solutions, one is to use a try/finally, putting the cleanup in the finally, and another is to put the logic in the tear down method. I tend to favor the tear-down, because it removes clutter from the test method.

Test should always fail fast. So how do we handle exceptions in happy-path tests? We don't.

Sunday, January 18, 2009

On testing data objects, the first step towards TDD

On testing data objects, the first step towards TDD

The simplest class behavior to test is the basic mapping of an object's properties either through it's constructors or through it's accessors. This translates to validating the correctness of the object creation, or validating the correctness of the object's modification.

Validating object construction

The first and easiest test cases involve the construction of an object. Usually the first test case involves validating and object's default values through a default constructor. Remember to question what an accessor should return if an object field is null.

Subsequent tests involve and validate that the construction parameters are correctly represented through the objects accessors.

I encourage validating a single object property per test. This makes it easier to identify what code is at fault when a test fails, it also facilitates defining test names which describe the expected result of the test.

Validating object modification

The next step in object testing, may well be the modification of properties. I am personally of the opinion that this should cause us to question our object modeling, but there are some valid uses for this, see bellow.

These tests involve constructing an object with one set of values, and validating that modification to that truely re-assigns the value. It is important to use test data that clearly demonstrates the modification of the field. So if the initial value is the value '1', the modification should assign '2'.

There is little interest in validating multiple subsequent modifications on a specific object instance unless the object supports some special caching or history behavior. In most cases and assignment is an assignment is an assignment. Dont clutter your tests with sequential validations.

Why validate the obvious case?

In the javabean and pre-version 3.0 C# approach to properties, an accessor is an explicit block of code. Consequently, it can contain bugs. This is rarely the case, but it does happen from time to time. Usually such bugs are quickly identified and fixed, and subsequently not repeated, but there are nonetheless reasons to validate them.

Remember that even if the property uses the trivial implementation, it remains a kind of method. Even C#'s properties are a kind of field-separated accessor with a signature that remains independent of the internal class data representation. This means that through the magic of refactoring, changes to the underlying structure of a class can continue to expose identical properties even if the origin of said properties has changed. Wouldnt it be nice if we could validate that a refactoring of a class' internal workings continued to provide the same external behavior? I certainly think so.

In C# 3.0, it is now possible to specify a property with a short-hand notation which automatically generates the trivial implementation. This does not invalidate my previous argument. The short-form syntax can be replaced with an elaborate evaluation logic without changing the class' signature. A simple test could help to catch a sudden change in behavior. Remember that tests are a kind of code-level contract. Changes in class behavior are a change in contract that must be re-negotiated by the programmer for all the affected parties including the test code.

How far should we go with testing the trivial implementation? Not very far. The tests are just a safe-guard against obvious violations, and the number of tests should reflect the complexity of the tested code. Only if the underling code grows in complexity, should the tests grow in number.

What about encapsulation?

Let me underline that this discussion focuses on data objects. Objects who's primary function is to store and transport data. Though there are reasons and contexts in which the are appropriate, and others in which they can be avoided, they remain the most frequent type of object that I've seen in application code bases. Regardless of your opinion on object modeling, they remain the easiest kind of object to test, and so for a TDD newbie, they are the low-hanging fruit.

Conclusion

Data objects are easy to test. The simplicity of their implementations make them an ideal starting point for test-driven development newbies. Start by validating object construction, then move on to object modification.