Feed4Junit的简单使用（五）数据来自动态约束数据

Feed4Junit官方地址：

http://databene.org/feed4junit.html

 

官方文档：

Generating constrained data dynamically

Feed4JUnit supports annotations defined in JSR 303, Java 7 and Benerator 0.7 for generating random data that matches constraints.

As an example see the @Pattern annotation as an example (javax.validation.constraints.Pattern):

@RunWith(Feeder.class)
public class RegexTest {

    @Test
    public void testSmoke(@Pattern(regexp = "[A-Z][a-z]{3,8}") String name) {
        System.out.println("name:" + name);
    }
    
} 

 

Annotation reference

Annotations marked bold are new in version 1.0:

Annotation	Java Package	Applicability	Description
@AssertFalse	javax.validation.constraints	parameter	Requires that a boolean parameter is false
@AssertTrue	javax.validation.constraints	parameter	Requires that a booöean parameter is true
@DecimalMin	javax.validation.constraints	parameter	Requires that a number parameter is greater than or equals a minimum value
@DecimalMax	javax.validation.constraints	parameter	Requires that a number parameter is less than or equals a maximum valueÂ
@Future	javax.validation.constraints	parameter	Requires that a Date parameter is in the future
@Min	javax.validation.constraints	parameter	Requires that an integral number parameter is greater than or equals a minimum value
@Max	javax.validation.constraints	parameter	Requires that an integral number parameter is less than or equals a maximum value
@NotNull	javax.validation.constraints	parameter	Requires that a parameter is not null
@Null	javax.validation.constraints	parameter	Requires that a parameter is null
@Past	javax.validation.constraints	parameter	Requires that a Date parameter is in the past
@Pattern	javax.validation.constraints	parameter	Requires that a String parameter matches a regular expression
@InvocationCount	org.databene.benerator.anno	method	Limits the number of invocations to a test method
@Source	org.databene.benerator.anno	method, parameter	Specifies a source file from which to read test data (e.g. CSV or Excel(TM) file)
@Offset	org.databene.benerator.anno	method, parameter	Makes Feed4Junit skip the first n data sets that are imported or generated
@Distribution	org.databene.benerator.anno	parameter	Specifies a distribution to use for a number parameter
@Granularity	org.databene.benerator.anno	parameter	Specifies the granularity of a number parameter (corresponds to Benerator's 'precision')
@Nullquota	org.databene.benerator.anno	parameter	Specifies the quota of null values to generate for a parameter
@Values	org.databene.benerator.anno	parameter	Specifies a comma-separated list of all supported values
@Last	org.databene.benerator.anno	parameter	Used for summary and cleanup functionality necessary after the last call to a test method: The last parameter of a mathod can be annotated with @Last and be made booloean. It will then receive a true value on the last invocation, otherwise false.
@Generator	org.databene.benerator.anno	method, parameter	Specifies a simple type generator for a parameter or an array generator for a complete parameter set
@Database	org.databene.benerator.anno	class, method	Defines a database to be used for data retrieval
@Bean	org.databene.benerator.anno	class, method	Defines a custom data generator to retrieve data from
@Equivalence	org.databene.benerator.anno	class, method	Advises Feed4JUnit to use the EquivalenceGeneratorFactory for all related test methods. It creates relatively small numbers of mean tests using mechanisms from border value and partition testing.
@Coverage	org.databene.benerator.anno	class, method	Advises Feed4JUnit to use the CoverageGeneratorFactory for all related test methods. It runs through all possible values and all possible combinations and produces a large number of tests.
@Stochastic	org.databene.benerator.anno	class, method	Advises Feed4JUnit to use the StochasticGeneratorFactory for all related test methods. It creates gentle datarandomly and provides for an unlimited number of tests.

 

 

 

Smoke Testing 

Performing smoke tests with random but valid data can strongly improve code coverage and exhibit bugs from very special data constellations. Even if you cannot simply predict each result of random data, you can check result constraints or at least check for runtime exceptions.

In FeedJUnit, you can use the @Stoachstic annotation to generate random data or the @Coverage annotation to first generate border values and then all values between. Be aware that you need to restrict the InvocationCount in many cases (unless you want to test billions of calls):

@RunWith(Feeder.class)
public class AddTest {

    @Test
    @Coverage
    @InvocationCount(100)
    public void testAdd(int param1, int param2) {
        try {
            int result = MyUtil.add(param1, param2);
        } catch (Exception e) {
            // accept application exceptions, fail on runtime exceptions
            // like NullPointerException
            if (e instanceof RuntimeException)
                throw e;
        }  
    }

}