With a good set of tests in place, refactoring code is much easier, as you can quickly gain a lot of confidence by running the tests again and making sure the code still passes.
As suites of tests grow, it's common to see duplication emerge. Like any code, tests should ideally be kept in a state that's easy to understand and maintain. So, you'll want to refactor your tests, too.
However, refactoring tests can be hard because you don't have tests for the tests.
How do you know that your refactoring of the tests was safe and you didn't accidentally remove one of the assertions?
If you intentionally break the code under test, the failing test can show you that your assertions are still working. For example, if you were refactoring methods in CombineHarvesterTest, you would alter CombineHarvester, making it return the wrong results.
CombineHarvesterTest
CombineHarvester
Check that the reason the tests are failing is because the assertions are failing as you'd expect them to. You can then (carefully) refactor the failing tests. If at any step they start passing, it immediately lets you know that the test is broken – undo! When you're done, remember to fix the code under test and make sure the tests pass again.(revert is your friend, but don't revert the tests!)
revert
Let's repeat that important point:
When you're done...remember to fix the code under test!
Summary
Remember to download this episode of Testing on the Toilet and post it in your office.
Zurich, Switzerland is the location of one of Google's largest engineering offices in Europe. We like to say it is no longer necessary to live in Silicon Valley to develop great software for Google, and the Zurich office has over 100 software engineers working on Google products and infrastructure. As a result the Zurich Test Engineering team is kept very busy.
Producing great software is the driving motivation for each member of the Test Engineering team, which is relatively small by industry standards. But we're growing quickly and are passionate about software development, software quality, and testing.
We work on several projects: some are customer-facing and some are infrastructure. We currently work on Google Maps, which has testers and developers in several of our offices around the world. Our team builds tools to check the consistency of the data feeds that support the local search features in Maps. Another project mainly developed in Zurich is Google Transit, which provides public transportation itineraries and schedule information for commuters who take trains, buses, and trams. On this project, we build tools to verify the proper alignment of the transportation layer of the map with the actual location coordinates of transit stops. We also focus on many projects related to Google’s infrastructure. For example, with Google Base API, we work with the Software Engineering team to measure response time and to track bottlenecks during large-scale bulk updates.
Our aim is to assign local test engineers to most projects developed in this office, so hiring for this team is always a priority. Candidates are from all over the world, and many different nationalities are represented in our office. Adapting to Zurich is quite easy, because it is already an international place: many companies have their headquarters here, and Zurich has been named the best city in the world for its quality of life in 2005, 2006, and 2007.
Michael Feathers defines the qualities of a good unit test as: “they run fast, they help us localize problems.” This can be hard to accomplish when your code accesses a database, hits another server, is time-dependent, etc.
By substituting custom objects for some of your module's dependencies, you can thoroughly test your code, increase your coverage, and still run in less than a second. You can even simulate rare scenarios like database failures and test your error handling code.
A variety of different terms are used to refer to these “custom objects”. In an effort to clarify the vocabulary, Gerard Meszaros provides the following definitions:
For example, to test a simple method like getIdPrefix() in the IdGetter class:
public class IdGetter { // Constructor omitted. public String getIdPrefix() { try { String s = db.selectString("select id from foo"); return s.substring(0, 5); } catch (SQLException e) { return ""; } }}
You could write:
db.execute("create table foo (id varchar(40))"); // db created in setUp(). db.execute("insert into foo (id) values ('hello world!')"); IdGetter getter = new IdGetter(db); assertEquals("hello", getter.getIdPrefix());
The test above works but takes a relatively long time to run (network access), can be unreliable (db machine might be down), and makes it hard to test for errors. You can avoid these pitfalls by using stubs:
public class StubDbThatReturnsId extends Database { public String selectString(String query) { return "hello world"; } } public class StubDbThatFails extends Database { public String selectString(String query) throws SQLException { throw new SQLException("Fake DB failure"); } } public void testReturnsFirstFiveCharsOfId() throws Exception { IdGetter getter = new IdGetter(new StubDbThatReturnsId()); assertEquals("hello", getter.getIdPrefix()); } public void testReturnsEmptyStringIfIdNotFound() throws Exception { IdGetter getter = new IdGetter(new StubDbThatFails()); assertEquals("", getter.getIdPrefix()); }
