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?
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
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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}")??? }
????
}?
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Annotation reference
Annotations marked?bold?are new in version 1.0:
AnnotationJava PackageApplicability
Description?
@AssertFalsejavax.validation.constraintsparameterRequires that a boolean parameter is false@AssertTruejavax.validation.constraintsparameter?Requires that a boo??ean parameter is true?@DecimalMinjavax.validation.constraintsparameter?Requires that a number parameter is greater than or equals a minimum value?@DecimalMax?javax.validation.constraintsparameter?Requires that a number parameter is less than or equals a maximum value???@Future?javax.validation.constraintsparameter?Requires that a Date parameter is in the future?@Min?javax.validation.constraintsparameter?Requires that an integral number parameter is greater than or equals a minimum value?@Max?javax.validation.constraintsparameter?Requires that an integral number parameter is less than or equals a maximum value?@NotNull?javax.validation.constraintsparameter?Requires that a parameter is not null?@Null?javax.validation.constraintsparameter?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?@InvocationCountorg.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.annomethod,
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?@Beanorg.databene.benerator.annoclass,
methodDefines 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.
@Stochasticorg.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.?
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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;
??????? }??
??? }
}
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