JUnit Theories
Theories allows one to write tests that apply to a (potentially infinite) set of data points rather than having to recreate the same test multiple times with different data or creating one test and iterating through your own collection of data values.
Here is an example:
Messaging client
First off is the interface for the messaging client. It has two lifecycle methods, start and stop which are self-explanatory, as well as methods for reading and sending a message which for brevity are just strings.
Status Enum
The following enum represents the status of the JMS connection. As can be seen there are numerous states so it would be cumbersome to work out all the possible combinations for each status transition. This is where Theories become so useful. More of that later.
Restart Manager
This class is used to restart a message client when it recognizes a status transition from FAILED to STARTED.
Test class
Finally here's the test class. Notice I'm using mock objects here using Mockito. As I'm programming to the interface of the messaging client not its implementation, I can make use of a mock and verify its behaviour. Presently as of JUnit 4.8, theories is still within an experimental package.
The most important bit of configuration is @Datapoints annotation. This sets up the data that'll be pass into the test methods. In this test I have two theory methods. One theory checks that the message client is restarted when there is a suitable status transition i.e. FAILED to STARTED. Another theory checks that the message client is not restarted if the status transition is not suitable. i.e. not FAILED to STARTED.
When you run the tests you'll find the all the combinations of the Status enums are passed to the test methods in question as shown below:
In this way, I ensure that all possible transitions are covered by my unit tests and the restart manager's behaviour is as required.
Conclusion
The use of theories allows tests to be devised that cover all possible combinations of data. This is in contrast to parameterized tests where the dataset to be passed to a test is strictly defined and the onus is on the developer to work out what data is needed for a particular range of tests. Each approach can be used in different situations as dictated by your requirements.