DDevelopment & Design

OVal – validate your models quickly and effortlessly!

Some time ago one of the projects at work required me to validate some Java POJOs. Theses were my model classes and I’ve been creating them from incoming WebService requests. One would say that XSD would be sufficient for the task, for parts of this va…Some time ago one of the projects at work required me to validate some Java POJOs. Theses were my model classes and I’ve been creating them from incoming WebService requests. One would say that XSD would be sufficient for the task, for parts of this va…

Some time ago one of the projects at work required me to validate some Java POJOs. Theses were my model classes and I’ve been creating them from incoming WebService requests. One would say that XSD would be sufficient for the task, for parts of this validations – sure, it would. But there were some advanced rules XSD would not handle, or would render the schema document very complicated.

Rules I needed to express were like:

  • person’s first_name and last_name should be of appropriate length – between 2 and 20, and additionally one could pass a zero-length string just to remove the previous value
  • state field should consist only defined values – as in dictionary value – this one would be completable with XSD’s enumerations, but would require often changing schema files and redistributing them to interested parties :(

The library I’ve decided to use for this task is OVal and it came out really nice! Read on to find out the details!

Oval is quite mature library that allows POJO validation, but is not JSR303 (bean validation) implementation. It has converters that enable it to understand those annotations, but I’m not sure about the compatibility.

I’ve tried only a subset of the available checks, among which were:

  • NotNull
  • NotEmpty
  • Length

There are many more, and their attributes give interesting ways to configure the validation process. But using them was rather easy and did not require to much brainstorming. What I really needed were custom checks. And in this area OVal shows it’s strength. Implementing a check is really easy.

I needed an annotation that would check a field against some values in a dictionary. If field’s value was in the given set, than the validation would succeed, if not, an exception would be thrown. To accomplish this task it is required to implement two classes: annotation class and check class – called by the validation engine on a given field.

Let’s start with our new annotation:

 

In the above snippet I’ve defined a check-annotation, that would be used like this:

 

You can pass file – containing dictionary values for this field. There is also message field in the annotation which is an error message returned by the validation engine of failed check – pretty handy. And can be expressed in .properties file as:

 

Placeholder, like context, will be replaced with correct values supplied by the validation engine.

Annotating a field is not enough. It is also needed to create a validator for this kind of check. The name of the class is already defined in DictionaryValue annotation, it is called DictionaryValueCheck and I’ve done this check this way:

 

What this basically does is:

  1. when file is set – read dictionary content from the file into map
  2. upon check request just lookup value in dictionary parsed from the input file

And that’s it!

For me Oval is really great tool. With it at ones disposal it is extremely easy to create any imaginable validation you need. This library is really easy to use and offers lots of handy features.

But perhaps I’m reinventing the wheel and all this can be done easily with some other library? Share Your opinion!

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DDevelopment & Design

Multi module Gradle project with IDE support

This article is a short how-to about multi-module project setup with usage of the Gradle automation build tool.

Here's how Rich Seller, a StackOverflow user, describes Gradle:
Gradle promises to hit the sweet spot between Ant and Maven. It uses Ivy's approach for dependency resolution. It allows for convention over configuration but also includes Ant tasks as first class citizens. It also wisely allows you to use existing Maven/Ivy repositories.
So why would one use yet another JVM build tool such as Gradle? The answer is simple: to avoid frustration involved by Ant or Maven.

Short story

I was fooling around with some fresh proof of concept and needed a build tool. I'm pretty familiar with Maven so created project from an artifact, and opened the build file, pom.xml for further tuning.
I had been using Grails with its own build system (similar to Gradle, btw) already for some time up then, so after quite a time without Maven, I looked on the pom.xml and found it to be really repulsive.

Once again I felt clearly: XML is not for humans.

After quick googling I found Gradle. It was still in beta (0.8 version) back then, but it's configured with Groovy DSL and that's what a human likes :)

Where are we

In the time Ant can be met but among IT guerrillas, Maven is still on top and couple of others like for example Ivy conquer for the best position, Gradle smoothly went into its mature age. It's now available in 1.3 version, released at 20th of November 2012. I'm glad to recommend it to anyone looking for relief from XML configured tools, or for anyone just looking for simple, elastic and powerful build tool.

Lets build

I have already written about basic project structure so I skip this one, reminding only the basic project structure:
<project root>

│

├── build.gradle

└── src

    ├── main

    │   ├── java

    │   └── groovy

    │

    └── test

        ├── java

        └── groovy
Have I just referred myself for the 1st time? Achievement unlocked! ;)

Gradle as most build tools is run from a command line with parameters. The main parameter for Gradle is a 'task name', for example we can run a command: gradle build.
There is no 'create project' task, so the directory structure has to be created by hand. This isn't a hassle though.
Java and groovy sub-folders aren't always mandatory. They depend on what compile plugin is used.

Parent project

Consider an example project 'the-app' of three modules, let say:
  1. database communication layer
  2. domain model and services layer
  3. web presentation layer
Our project directory tree will look like:
the-app

│

├── dao-layer

│   └── src

│

├── domain-model

│   └── src

│

├── web-frontend

│   └── src

│

├── build.gradle

└── settings.gradle
the-app itself has no src sub-folder as its purpose is only to contain sub-projects and build configuration. If needed it could've been provided with own src though.

To glue modules we need to fill settings.gradle file under the-app directory with a single line of content specifying module names:
include 'dao-layer', 'domain-model', 'web-frontend'
Now the gradle projects command can be executed to obtain such a result:
:projects


------------------------------------------------------------

Root project

------------------------------------------------------------


Root project 'the-app'

+--- Project ':dao-layer'

+--- Project ':domain-model'

\--- Project ':web-frontend'
...so we know that Gradle noticed the modules. However gradle build command won't run successful yet because build.gradle file is still empty.

Sub project

As in Maven we can create separate build config file per each module. Let say we starting from DAO layer.
Thus we create a new file the-app/dao-layer/build.gradle with a line of basic build info (notice the new build.gradle was created under sub-project directory):
apply plugin: 'java'
This single line of config for any of modules is enough to execute gradle build command under the-app directory with following result:
:dao-layer:compileJava

:dao-layer:processResources UP-TO-DATE

:dao-layer:classes

:dao-layer:jar

:dao-layer:assemble

:dao-layer:compileTestJava UP-TO-DATE

:dao-layer:processTestResources UP-TO-DATE

:dao-layer:testClasses UP-TO-DATE

:dao-layer:test

:dao-layer:check

:dao-layer:build


BUILD SUCCESSFUL


Total time: 3.256 secs
To use Groovy plugin slightly more configuration is needed:
apply plugin: 'groovy'


repositories {

    mavenLocal()

    mavenCentral()

}


dependencies {

    groovy 'org.codehaus.groovy:groovy-all:2.0.5'

}
At lines 3 to 6 Maven repositories are set. At line 9 dependency with groovy library version is specified. Of course plugin as 'java', 'groovy' and many more can be mixed each other.

If we have settings.gradle file and a build.gradle file for each module, there is no need for parent the-app/build.gradle file at all. Sure that's true but we can go another, better way.

One file to rule them all

Instead of creating many build.gradle config files, one per each module, we can use only the parent's one and make it a bit more juicy. So let us move the the-app/dao-layer/build.gradle a level up to the-app/build-gradle and fill it with new statements to achieve full project configuration:
def langLevel = 1.7


allprojects {


    apply plugin: 'idea'


    group = 'com.tamashumi'

    version = '0.1'

}


subprojects {


    apply plugin: 'groovy'


    sourceCompatibility = langLevel

    targetCompatibility = langLevel


    repositories {

        mavenLocal()

        mavenCentral()

    }


    dependencies {

        groovy 'org.codehaus.groovy:groovy-all:2.0.5'

        testCompile 'org.spockframework:spock-core:0.7-groovy-2.0'

    }

}


project(':dao-layer') {


    dependencies {

        compile 'org.hibernate:hibernate-core:4.1.7.Final'

    }

}


project(':domain-model') {


    dependencies {

        compile project(':dao-layer')

    }

}


project(':web-frontend') {


    apply plugin: 'war'


    dependencies {

        compile project(':domain-model')

        compile 'org.springframework:spring-webmvc:3.1.2.RELEASE'

    }

}


idea {

    project {

        jdkName = langLevel

        languageLevel = langLevel

    }

}
At the beginning simple variable langLevel is declared. It's worth knowing that we can use almost any Groovy code inside build.gradle file, statements like for example if conditions, for/while loops, closures, switch-case, etc... Quite an advantage over inflexible XML, isn't it?

Next the allProjects block. Any configuration placed in it will influence - what a surprise - all projects, so the parent itself and sub-projects (modules). Inside of the block we have the IDE (Intellij Idea) plugin applied which I wrote more about in previous article (look under "IDE Integration" heading). Enough to say that with this plugin applied here, command gradle idea will generate Idea's project files with modules structure and dependencies. This works really well and plugins for other IDEs are available too.
Remaining two lines at this block define group and version for the project, similar as this is done by Maven.

After that subProjects block appears. It's related to all modules but not the parent project. So here the Groovy language plugin is applied, as all modules are assumed to be written in Groovy.
Below source and target language level are set.
After that come references to standard Maven repositories.
At the end of the block dependencies to groovy version and test library - Spock framework.

Following blocks, project(':module-name'), are responsible for each module configuration. They may be omitted unless allProjects or subProjects configure what's necessary for a specific module. In the example per module configuration goes as follow:
  • Dao-layer module has dependency to an ORM library - Hibernate
  • Domain-model module relies on dao-layer as a dependency. Keyword project is used here again for a reference to other module.
  • Web-frontend applies 'war' plugin which build this module into java web archive. Besides it referes to domain-model module and also use Spring MVC framework dependency.

At the end in idea block is basic info for IDE plugin. Those are parameters corresponding to the Idea's project general settings visible on the following screen shot.


jdkName should match the IDE's SDK name otherwise it has to be set manually under IDE on each Idea's project files (re)generation with gradle idea command.

Is that it?

In the matter of simplicity - yes. That's enough to automate modular application build with custom configuration per module. Not a rocket science, huh? Think about Maven's XML. It would take more effort to setup the same and still achieve less expressible configuration quite far from user-friendly.

Check the online user guide for a lot of configuration possibilities or better download Gradle and see the sample projects.
As a tasty bait take a look for this short choice of available plugins:
  • java
  • groovy
  • scala
  • cpp
  • eclipse
  • netbeans
  • ida
  • maven
  • osgi
  • war
  • ear
  • sonar
  • project-report
  • signing
and more, 3rd party plugins...
DDevelopment & Design

Spock basics

Spock (homepage) is like its authors say 'testing and specification framework'. Spock combines very elegant and natural syntax with the powerful capabilities. And what is most important it is easy to use.

One note at the very beginning: I assume that you are already familiar with principles of Test Driven Development and you know how to use testing framework like for example JUnit.

So how can I start?


Writing spock specifications is very easy. We need basic configuration of Spock and Groovy dependencies (if you are using mavenized project with Eclipse look to my previous post: Spock, Java and Maven). Once we have everything set up and running smooth we can write our first specs (spec or specification is equivalent for test class in other frameworks like JUnit of TestNG).

What is great with Spock is fact that we can use it to test both Groovy projects and pure Java projects or even mixed projects.


Let's go!


Every spec class must inherit from spock.lang.Specification class. Only then test runner will recognize it as test class and start tests. We will write few specs for this simple class: User class and few tests not connected with this particular class.

We start with defining our class:
import spock.lang.*

class UserSpec extends Specification {

}
Now we can proceed to defining test fixtures and test methods.

All activites we want to perform before each test method, are to be put in def setup() {...} method and everything we want to be run after each test should be put in def cleanup() {...} method (they are equivalents for JUnit methods with @Before and @After annotations).

It can look like this:
class UserSpec extends Specification {
    User user
    Document document

    def setup() {
        user = new User()
        document = DocumentTestFactory.createDocumentWithTitle("doc1")
    }

    def cleanup() {

    }
}
Of course we can use field initialization for instantiating test objects:
class UserSpec extends Specification {
    User user = new User()
    Document document = DocumentTestFactory.createDocumentWithTitle("doc1")

    def setup() {

    }

    def cleanup() {

    }
}

What is more readable or preferred? It is just a matter of taste because according to Spock docs behaviour is the same in these two cases.

It is worth mentioning that JUnit @BeforeClass/@AfterClass are also present in Spock as def setupSpec() {...} and def cleanupSpec() {...}. They will be runned before first test and after last test method.


First tests


In Spock every method in specification class, expect setup/cleanup, is treated by runner as a test method (unless you annotate it with @Ignore).

Very interesting feature of Spock and Groovy is ability to name methods with full sentences just like regular strings:
class UserSpec extends Specification {
    // ...

    def "should assign coment to user"() {
        // ...
    }
}
With such naming convention we can write real specification and include details about specified behaviour in method name, what is very convenient when reading test reports and analyzing errors.

Test method (also called feature method) is logically divided into few blocks, each with its own purpose. Blocks are defined like labels in Java (but they are transformed with Groovy AST transform features) and some of them must be put in code in specific order.

Most basic and common schema for Spock test is:
class UserSpec extends Specification {
    // ...

    def "should assign coment to user"() {
        given:
            // do initialization of test objects
        when:
            // perform actions to be tested
        then:
            // collect and analyze results
    }
}

But there are more blocks like:
  • setup
  • expect
  • where
  • cleanup
In next section I am going to describe each block shortly with little examples.

given block

This block is used to setup test objects and their state. It has to be first block in test and cannot be repeated. Below is little example how can it be used:
class UserSpec extends Specification {
    // ...

    def "should add project to user and mark user as project's owner"() {
        given:
            User user = new User()
            Project project = ProjectTestFactory.createProjectWithName("simple project")
        // ...
    }
}

In this code given block contains initialization of test objects and nothing more. We create simple user without any specified attributes and project with given name. In case when some of these objects could be reused in more feature methods, it could be worth putting initialization in setup method.

when and then blocks

When block contains action we want to test (Spock documentation calls it 'stimulus'). This block always occurs in pair with then block, where we are verifying response for satisfying certain conditions. Assume we have this simple test case:
class UserSpec extends Specification {
    // ...

    def "should assign user to comment when adding comment to user"() {
        given:
            User user = new User()
            Comment comment = new Comment()
        when:
            user.addComment(comment)
        then:
            comment.getUserWhoCreatedComment().equals(user)
    }

    // ...
}

In when block there is a call of tested method and nothing more. After we are sure our action was performed, we can check for desired conditions in then block.

Then block is very well structured and its every line is treated by Spock as boolean statement. That means, Spock expects that we write instructions containing comparisons and expressions returning true or false, so we can create then block with such statements:
user.getName() == "John"
user.getAge() == 40
!user.isEnabled()
Each of lines will be treated as single assertion and will be evaluated by Spock.

Sometimes we expect that our method throws an exception under given circumstances. We can write test for it with use of thrown method:
class CommentSpec extends Specification {
    def "should throw exception when adding null document to comment"() {
        given:
            Comment comment = new Comment()
        when:
            comment.setCommentedDocument(null)
        then:
            thrown(RuntimeException)
    }
}

In this test we want to make sure that passing incorrect parameters is correctly handled by tested method and that method throws an exception in response. In case you want to be certain that method does not throw particular exception, simply use notThrown method.


expect block

Expect block is primarily used when we do not want to separate when and then blocks because it is unnatural. It is especially useful for simple test (and according to TDD rules all test should be simple and short) with only one condition to check, like in this example (it is simple but should show the idea):
def "should create user with given name"() {
    given:
        User user = UserTestFactory.createUser("john doe")
    expect:
        user.getName() == "john doe"
}



More blocks!


That were very simple tests with standard Spock test layout and canonical divide into given/when/then parts. But Spock offers more possibilities in writing tests and provides more blocks.


setup/cleanup blocks

These two blocks have the very same functionality as the def setup and def cleanup methods in specification. They allow to perform some actions before test and after test. But unlike these methods (which are shared between all tests) blocks work only in methods they are defined in. 


where - easy way to create readable parameterized tests

Very often when we create unit tests there is a need to "feed" them with sample data to test various cases and border values. With Spock this task is very easy and straighforward. To provide test data to feature method, we need to use where block. Let's take a look at little the piece of code:

def "should successfully validate emails with valid syntax"() {
    expect:
        emailValidator.validate(email) == true
    where:
        email }

In this example, Spock creates variable called email which is used when calling method being tested. Internally feature method is called once, but framework iterates over given values and calls expect/when block as many times as there are values (however, if we use @Unroll annotation Spock can create separate run for each of given values, more about it in one of next examples).

Now, lets assume that we want our feature method to test both successful and failure validations. To achieve that goal we can create few parameterized variables for both input parameter and expected result. Here is a little example:

def "should perform validation of email addresses"() {
    expect:
        emailValidator.validate(email) == result
    where:
        email         result }
Well, it looks nice, but Spock can do much better. It offers tabular format of defining parameters for test what is much more readable and natural. Lets take a look:
def "should perform validation of email addresses"() {
    expect:
        emailValidator.validate(email) == result
    where:
        email           | result
        "WTF"           | false
        "@domain"       | false
        "foo@bar.com"   | true
        "a@test"        | false
}
In this code, each column of our "table" is treated as a separate variable and rows are values for subsequent test iterations.

Another useful feature of Spock during parameterizing test is its ability to "unroll" each parameterized test. Feature method from previous example could be defined as (the body stays the same, so I do not repeat it):
@Unroll("should validate email #email")
def "should perform validation of email addresses"() {
    // ...
}
With that annotation, Spock generate few methods each with its own name and run them separately. We can use symbols from where blocks in @Unroll argument by preceding it with '#' sign what is a signal to Spock to use it in generated method name.


What next?


Well, that was just quick and short journey  through Spock and its capabilities. However, with that basic tutorial you are ready to write many unit tests. In one of my future posts I am going to describe more features of Spock focusing especially on its mocking abilities.