Meet Sputnik – static code analyser for Gerrit

Sputnik runs Checkstyle, PMD and FindBugs for your Gerrit patchsets I am happy to announce a first release of Sputnik! It is a static code analyzer that runs Checkstyle, PMD and FindBugs for your Gerrit patchsets. Its main advantage over my previous pr…

Sputnik runs Checkstyle, PMD and FindBugs for your Gerrit patchsets

I am happy to announce a first release of Sputnik! It is a static code analyzer that runs Checkstyle, PMD and FindBugs for your Gerrit patchsets. Its main advantage over my previous project Sonar Gerrit plugin is that Sputnik is a small, lightweight and standalone Java application. You don’t need any other software to run it. It bundles Checkstyle, PMD and FindBugs jars within distribution zip.

Workflow

Sputnik is intended to use with Gerrit and Continous Integration server, i. e. Jenkins. It works like this:

Your CI server is updated by ssh that a new patch is submitted to Gerrit. CI fetches this patch and builds a while project. After a build, CI server reports its result to Gerrit. It’s time for Sputnik now.

Sputnik runs regardless of build result (you can change that in your CI configuration). Sputnik fetches patchset’s file list from Gerrit over HTTP REST API. Then it runs an analysis only on these files! Even if your project is huge, analysis on several files takes only seconds. Sputnik collects comments from all three analysers: Checkstyle, PMD and FindBugs. It sends back all comments to Gerrit via HTTP REST API back. It’s very simple and very fast!

Installation and configuration

First, you need to build https://github.com/TouK/sputnik master or download distribution zip from here: sputnik-1.0.zip. Go to you CI server and extract it to a directory of your choice. Remember that a user you run CI builds needs to have an access rights to this directory (in my case it’s simply a jenkins user). Then you need to prepare your configuration file and write this file to the same directory as unzipped distribution. It is a simple Java properties file, which is pretty self-explanatory. Here is an example:

gerrit.host=gerrit.yourcompany.com
gerrit.port=8080
gerrit.username=sputnik
gerrit.password=Pa$$wo4d
checkstyle.enabled=true
checkstyle.configurationFile=/opt/jenkins/sputnik/checkstyle.xml
checkstyle.propertiesFile=
pmd.enabled=true
pmd.ruleSets=/opt/jenkins/sputnik/pmd.xml
findbugs.enabled=true
findbugs.includeFilter=/opt/jenkins/sputnik/findbugs.xml
findbugs.excludeFilter=

Now you need to configure you CI server to actually run Sputnik after a build. It is very simple for Jenkins, just add a Post-Build Step. You can adjust if Sputnik runs only on successful build or for every build – use radio buttons for this:

Last line with exit 0 is a workaround for a clean exit, even if Sputnik fails for some reason. Exit 0 guarantees you that result of this step doesn’t affect overall build result.

Summary

This is an example screenshot of Sputnik’s comments:

Sputnik always reports +1 as a result. It can be lacking in some network and authorisation configuration. But it’s open source so please submit issues and patches to its github page: https://github.com/TouK/sputnik.

Your feedback and pull requests are heartly welcome!

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Integration testing custom validation constraints in Jersey 2

I recently joined a team trying to switch a monolithic legacy system into set of RESTful services in Java. They decided to use latest 2.x version of Jersey as a REST container which was not a first choice for me, since I’m not a big fan of JSR-* specs. But now I must admit that JAX-RS 2.x is doing things right: requires almost zero boilerplate code, support auto-discovery of features and prefers convention over configuration like other modern frameworks. Since the spec is still young, it’s hard to find good tutorials and kick-off projects with some working code. I created jersey2-starter project on GitHub which can be used as starting point for your own production-ready RESTful service. In this post I’d like to cover how to implement and integration test your own validation constraints of REST resources.

Custom constraints

One of the issues which bothers me when coding REST in Java is littering your class model with annotations. Suppose you want to build a simple Todo list REST service, when using Jackson, validation and Spring Data, you can easily end up with this as your entity class:

@Document
public class Todo {
    private Long id;
    @NotNull
    private String description;
    @NotNull
    private Boolean completed;
    @NotNull
    private DateTime dueDate;

    @JsonCreator
    public Todo(@JsonProperty("description") String description, @JsonProperty("dueDate") DateTime dueDate) {
        this.description = description;
        this.dueDate = dueDate;
        this.completed = false;
    }
    // getters and setters
}

Your domain model is now effectively blured by messy annotations almost everywhere. Let’s see what we can do with validation constraints (@NotNulls). Some may say that you could introduce some DTO layer with own validation rules, but it conflicts for me with pure REST API design, which stands that you operate on resources which should map to your domain classes. On the other hand - what does it mean that Todo object is valid? When you create a Todo you should provide a description and due date, but what when you’re updating? You should be able to change any of description, due date (postponing) and completion flag (marking as done) - but you should provide at least one of these as valid modification. So my idea is to introduce custom validation constraints, different ones for creation and modification:

@Target({TYPE, PARAMETER})
@Retention(RUNTIME)
@Constraint(validatedBy = ValidForCreation.Validator.class)
public @interface ValidForCreation {
    //...
    class Validator implements ConstraintValidator<ValidForCreation, Todo> {
    /...
        @Override
        public boolean isValid(Todo todo, ConstraintValidatorContext constraintValidatorContext) {
            return todo != null
                && todo.getId() == null
                && todo.getDescription() != null
                && todo.getDueDate() != null;
        }
    }
}

@Target({TYPE, PARAMETER})
@Retention(RUNTIME)
@Constraint(validatedBy = ValidForModification.Validator.class)
public @interface ValidForModification {
    //...
    class Validator implements ConstraintValidator<ValidForModification, Todo> {
    /...
        @Override
        public boolean isValid(Todo todo, ConstraintValidatorContext constraintValidatorContext) {
            return todo != null
                && todo.getId() == null
                && (todo.getDescription() != null || todo.getDueDate() != null || todo.isCompleted() != null);
        }
    }
}

And now you can move validation annotations to the definition of a REST endpoint:

@POST
@Consumes(APPLICATION_JSON)
public Response create(@ValidForCreation Todo todo) {...}

@PUT
@Consumes(APPLICATION_JSON)
public Response update(@ValidForModification Todo todo) {...}

And now you can remove those NotNulls from your model.

Integration testing

There are in general two approaches to integration testing:

  • test is being run on separate JVM than the app, which is deployed on some other integration environment
  • test deploys the application programmatically in the setup block.

Both of these have their pros and cons, but for small enough servoces, I personally prefer the second approach. It’s much easier to setup and you have only one JVM started, which makes debugging really easy. You can use a generic framework like Arquillian for starting your application in a container environment, but I prefer simple solutions and just use emdedded Jetty. To make test setup 100% production equivalent, I’m creating full Jetty’s WebAppContext and have to resolve all runtime dependencies for Jersey auto-discovery to work. This can be simply achieved with Maven resolved from Shrinkwrap - an Arquillian subproject:

    WebAppContext webAppContext = new WebAppContext();
    webAppContext.setResourceBase("src/main/webapp");
    webAppContext.setContextPath("/");
    File[] mavenLibs = Maven.resolver().loadPomFromFile("pom.xml")
                .importCompileAndRuntimeDependencies()
                .resolve().withTransitivity().asFile();
    for (File file: mavenLibs) {
        webAppContext.getMetaData().addWebInfJar(new FileResource(file.toURI()));
    }
    webAppContext.getMetaData().addContainerResource(new FileResource(new File("./target/classes").toURI()));

    webAppContext.setConfigurations(new Configuration[] {
        new AnnotationConfiguration(),
        new WebXmlConfiguration(),
        new WebInfConfiguration()
    });
    server.setHandler(webAppContext);

(this Stackoverflow thread inspired me a lot here)

Now it’s time for the last part of the post: parametrizing our integration tests. Since we want to test validation constraints, there are many edge paths to check (and make your code coverage close to 100%). Writing one test per each case could be a bad idea. Among the many solutions for JUnit I’m most convinced to the Junit Params by Pragmatists team. It’s really simple and have nice concept of JQuery-like helper for creating providers. Here is my tests code (I’m also using builder pattern here to create various kinds of Todos):

@Test
@Parameters(method = "provideInvalidTodosForCreation")
public void shouldRejectInvalidTodoWhenCreate(Todo todo) {
    Response response = createTarget().request().post(Entity.json(todo));

    assertThat(response.getStatus()).isEqualTo(BAD_REQUEST.getStatusCode());
}

private static Object[] provideInvalidTodosForCreation() {
    return $(
        new TodoBuilder().withDescription("test").build(),
        new TodoBuilder().withDueDate(DateTime.now()).build(),
        new TodoBuilder().withId(123L).build(),
        new TodoBuilder().build()
    );
}

OK, enough of reading, feel free to clone the project and start writing your REST services!

I recently joined a team trying to switch a monolithic legacy system into set of RESTful services in Java. They decided to use latest 2.x version of Jersey as a REST container which was not a first choice for me, since I’m not a big fan of JSR-* specs. But now I must admit that JAX-RS 2.x is doing things right: requires almost zero boilerplate code, support auto-discovery of features and prefers convention over configuration like other modern frameworks. Since the spec is still young, it’s hard to find good tutorials and kick-off projects with some working code. I created jersey2-starter project on GitHub which can be used as starting point for your own production-ready RESTful service. In this post I’d like to cover how to implement and integration test your own validation constraints of REST resources.

Custom constraints

One of the issues which bothers me when coding REST in Java is littering your class model with annotations. Suppose you want to build a simple Todo list REST service, when using Jackson, validation and Spring Data, you can easily end up with this as your entity class:

@Document
public class Todo {
    private Long id;
    @NotNull
    private String description;
    @NotNull
    private Boolean completed;
    @NotNull
    private DateTime dueDate;

    @JsonCreator
    public Todo(@JsonProperty("description") String description, @JsonProperty("dueDate") DateTime dueDate) {
        this.description = description;
        this.dueDate = dueDate;
        this.completed = false;
    }
    // getters and setters
}

Your domain model is now effectively blured by messy annotations almost everywhere. Let’s see what we can do with validation constraints (@NotNulls). Some may say that you could introduce some DTO layer with own validation rules, but it conflicts for me with pure REST API design, which stands that you operate on resources which should map to your domain classes. On the other hand - what does it mean that Todo object is valid? When you create a Todo you should provide a description and due date, but what when you’re updating? You should be able to change any of description, due date (postponing) and completion flag (marking as done) - but you should provide at least one of these as valid modification. So my idea is to introduce custom validation constraints, different ones for creation and modification:

@Target({TYPE, PARAMETER})
@Retention(RUNTIME)
@Constraint(validatedBy = ValidForCreation.Validator.class)
public @interface ValidForCreation {
    //...
    class Validator implements ConstraintValidator<ValidForCreation, Todo> {
    /...
        @Override
        public boolean isValid(Todo todo, ConstraintValidatorContext constraintValidatorContext) {
            return todo != null
                && todo.getId() == null
                && todo.getDescription() != null
                && todo.getDueDate() != null;
        }
    }
}

@Target({TYPE, PARAMETER})
@Retention(RUNTIME)
@Constraint(validatedBy = ValidForModification.Validator.class)
public @interface ValidForModification {
    //...
    class Validator implements ConstraintValidator<ValidForModification, Todo> {
    /...
        @Override
        public boolean isValid(Todo todo, ConstraintValidatorContext constraintValidatorContext) {
            return todo != null
                && todo.getId() == null
                && (todo.getDescription() != null || todo.getDueDate() != null || todo.isCompleted() != null);
        }
    }
}

And now you can move validation annotations to the definition of a REST endpoint:

@POST
@Consumes(APPLICATION_JSON)
public Response create(@ValidForCreation Todo todo) {...}

@PUT
@Consumes(APPLICATION_JSON)
public Response update(@ValidForModification Todo todo) {...}

And now you can remove those NotNulls from your model.

Integration testing

There are in general two approaches to integration testing:

  • test is being run on separate JVM than the app, which is deployed on some other integration environment
  • test deploys the application programmatically in the setup block.

Both of these have their pros and cons, but for small enough servoces, I personally prefer the second approach. It’s much easier to setup and you have only one JVM started, which makes debugging really easy. You can use a generic framework like Arquillian for starting your application in a container environment, but I prefer simple solutions and just use emdedded Jetty. To make test setup 100% production equivalent, I’m creating full Jetty’s WebAppContext and have to resolve all runtime dependencies for Jersey auto-discovery to work. This can be simply achieved with Maven resolved from Shrinkwrap - an Arquillian subproject:

    WebAppContext webAppContext = new WebAppContext();
    webAppContext.setResourceBase("src/main/webapp");
    webAppContext.setContextPath("/");
    File[] mavenLibs = Maven.resolver().loadPomFromFile("pom.xml")
                .importCompileAndRuntimeDependencies()
                .resolve().withTransitivity().asFile();
    for (File file: mavenLibs) {
        webAppContext.getMetaData().addWebInfJar(new FileResource(file.toURI()));
    }
    webAppContext.getMetaData().addContainerResource(new FileResource(new File("./target/classes").toURI()));

    webAppContext.setConfigurations(new Configuration[] {
        new AnnotationConfiguration(),
        new WebXmlConfiguration(),
        new WebInfConfiguration()
    });
    server.setHandler(webAppContext);

(this Stackoverflow thread inspired me a lot here)

Now it’s time for the last part of the post: parametrizing our integration tests. Since we want to test validation constraints, there are many edge paths to check (and make your code coverage close to 100%). Writing one test per each case could be a bad idea. Among the many solutions for JUnit I’m most convinced to the Junit Params by Pragmatists team. It’s really simple and have nice concept of JQuery-like helper for creating providers. Here is my tests code (I’m also using builder pattern here to create various kinds of Todos):

@Test
@Parameters(method = "provideInvalidTodosForCreation")
public void shouldRejectInvalidTodoWhenCreate(Todo todo) {
    Response response = createTarget().request().post(Entity.json(todo));

    assertThat(response.getStatus()).isEqualTo(BAD_REQUEST.getStatusCode());
}

private static Object[] provideInvalidTodosForCreation() {
    return $(
        new TodoBuilder().withDescription("test").build(),
        new TodoBuilder().withDueDate(DateTime.now()).build(),
        new TodoBuilder().withId(123L).build(),
        new TodoBuilder().build()
    );
}

OK, enough of reading, feel free to clone the project and start writing your REST services!

How to use mocks in controller tests

Even since I started to write tests for my Grails application I couldn't find many articles on using mocks. Everyone is talking about tests and TDD but if you search for it there isn't many articles.

Today I want to share with you a test with mocks for a simple and complete scenario. I have a simple application that can fetch Twitter tweets and present it to user. I use REST service and I use GET to fetch tweets by id like this: http://api.twitter.com/1/statuses/show/236024636775735296.json. You can copy and paste it into your browser to see a result.

My application uses Grails 2.1 with spock-0.6 for tests. I have TwitterReaderService that fetches tweets by id, then I parse a response into my Tweet class.


class TwitterReaderService {
Tweet readTweet(String id) throws TwitterError {
try {
String jsonBody = callTwitter(id)
Tweet parsedTweet = parseBody(jsonBody)
return parsedTweet
} catch (Throwable t) {
throw new TwitterError(t)
}
}

private String callTwitter(String id) {
// TODO: implementation
}

private Tweet parseBody(String jsonBody) {
// TODO: implementation
}
}

class Tweet {
String id
String userId
String username
String text
Date createdAt
}

class TwitterError extends RuntimeException {}

TwitterController plays main part here. Users call show action along with id of a tweet. This action is my subject under test. I've implemented some basic functionality. It's easier to focus on it while writing tests.


class TwitterController {
def twitterReaderService

def index() {
}

def show() {
Tweet tweet = twitterReaderService.readTweet(params.id)
if (tweet == null) {
flash.message = 'Tweet not found'
redirect(action: 'index')
return
}

[tweet: tweet]
}
}

Let's start writing a test from scratch. Most important thing here is that I use mock for my TwitterReaderService. I do not construct new TwitterReaderService(), because in this test I test only TwitterController. I am not interested in injected service. I know how this service is supposed to work and I am not interested in internals. So before every test I inject a twitterReaderServiceMock into controller:


import grails.test.mixin.TestFor
import spock.lang.Specification

@TestFor(TwitterController)
class TwitterControllerSpec extends Specification {
TwitterReaderService twitterReaderServiceMock = Mock(TwitterReaderService)

def setup() {
controller.twitterReaderService = twitterReaderServiceMock
}
}

Now it's time to think what scenarios I need to test. This line from TwitterReaderService is the most important:


Tweet readTweet(String id) throws TwitterError

You must think of this method like a black box right now. You know nothing of internals from controller's point of view. You're only interested what can be returned for you:

  • a TwitterError can be thrown
  • null can be returned
  • Tweet instance can be returned

This list is your test blueprint. Now answer a simple question for each element: "What do I want my controller to do in this situation?" and you have plan test:

  • show action should redirect to index if TwitterError is thrown and inform about error
  • show action should redirect to index and inform if tweet is not found
  • show action should show found tweet

That was easy and straightforward! And now is the best part: we use twitterReaderServiceMock to mock each of these three scenarios!

In Spock there is a good documentation about interaction with mocks. You declare what methods are called, how many times, what parameters are given and what should be returned. Remember a black box? Mock is your black box with detailed instruction, e.g.: I expect you that if receive exactly one call to readTweet with parameter '1' then you should throw me a TwitterError. Rephrase this sentence out loud and look at this:


1 * twitterReaderServiceMock.readTweet('1') >> { throw new TwitterError() }

This is a valid interaction definition on mock! It's that easy! Here is a complete test that fails for now:


import grails.test.mixin.TestFor
import spock.lang.Specification

@TestFor(TwitterController)
class TwitterControllerSpec extends Specification {
TwitterReaderService twitterReaderServiceMock = Mock(TwitterReaderService)

def setup() {
controller.twitterReaderService = twitterReaderServiceMock
}

def "show should redirect to index if TwitterError is thrown"() {
given:
controller.params.id = '1'
when:
controller.show()
then:
1 * twitterReaderServiceMock.readTweet('1') >> { throw new TwitterError() }
0 * _._
flash.message == 'There was an error on fetching your tweet'
response.redirectUrl == '/twitter/index'
}
}

| Failure: show should redirect to index if TwitterError is thrown(pl.refaktor.twitter.TwitterControllerSpec)
| pl.refaktor.twitter.TwitterError
at pl.refaktor.twitter.TwitterControllerSpec.show should redirect to index if TwitterError is thrown_closure1(TwitterControllerSpec.groovy:29)

You may notice 0 * _._ notation. It says: I don't want any other mocks or any other methods called. Fail this test if something is called! It's a good practice to ensure that there are no more interactions than you want.

Ok, now I need to implement controller logic to handle TwitterError.


class TwitterController {

def twitterReaderService

def index() {
}

def show() {
Tweet tweet

try {
tweet = twitterReaderService.readTweet(params.id)
} catch (TwitterError e) {
log.error(e)
flash.message = 'There was an error on fetching your tweet'
redirect(action: 'index')
return
}

[tweet: tweet]
}
}

My tests passes! We have two scenarios left. Rule stays the same: TwitterReaderService returns something and we test against it. So this line is the heart of each test, change only returned values after >>:


1 * twitterReaderServiceMock.readTweet('1') >> { throw new TwitterError() }

Here is a complete test for three scenarios and controller that passes it.


import grails.test.mixin.TestFor
import spock.lang.Specification

@TestFor(TwitterController)
class TwitterControllerSpec extends Specification {

TwitterReaderService twitterReaderServiceMock = Mock(TwitterReaderService)

def setup() {
controller.twitterReaderService = twitterReaderServiceMock
}

def "show should redirect to index if TwitterError is thrown"() {
given:
controller.params.id = '1'
when:
controller.show()
then:
1 * twitterReaderServiceMock.readTweet('1') >> { throw new TwitterError() }
0 * _._
flash.message == 'There was an error on fetching your tweet'
response.redirectUrl == '/twitter/index'
}

def "show should inform about not found tweet"() {
given:
controller.params.id = '1'
when:
controller.show()
then:
1 * twitterReaderServiceMock.readTweet('1') >> null
0 * _._
flash.message == 'Tweet not found'
response.redirectUrl == '/twitter/index'
}


def "show should show found tweet"() {
given:
controller.params.id = '1'
when:
controller.show()
then:
1 * twitterReaderServiceMock.readTweet('1') >> new Tweet()
0 * _._
flash.message == null
response.status == 200
}
}

class TwitterController {

def twitterReaderService

def index() {
}

def show() {
Tweet tweet

try {
tweet = twitterReaderService.readTweet(params.id)
} catch (TwitterError e) {
log.error(e)
flash.message = 'There was an error on fetching your tweet'
redirect(action: 'index')
return
}

if (tweet == null) {
flash.message = 'Tweet not found'
redirect(action: 'index')
return
}

[tweet: tweet]
}
}

The most important thing here is that we've tested controller-service interaction without logic implementation in service! That's why mock technique is so useful. It decouples your dependencies and let you focus on exactly one subject under test. Happy testing!