How to manage dependencies in a Gradle multi-project build


gradleI’ve been a fan of the Gradle build tool from quite early on. Its potential was clear even before the 1.0 version, when changes were regularly breaking. Today, upgrading rarely cause surprises. The tool has become mature and performs well.

Gradle includes a powerful dependency management system that can work with Maven and Ivy repositories as well as local file system dependencies.

During my work with Gradle I’ve come to rely on a pattern for managing dependencies in a multi-project build that I want to share. This pattern consists of two key practices:

  1. Centralize dependency declarations in build.gradle
  2. Centralize dependency version declarations in

Both practices are examples of applying software development best practices like DRY to the code that makes up the Gradle build. Let’s look at them in some more detail.

Centralize dependency declarations

In the root project’s build.gradle file, declare a new configuration for each dependency used in the entire project. In each sub-project that uses the dependency, declare that the compile (or testCompile, etc) configuration extends the configuration for the dependency:

subprojects {
  configurations {

  dependencies {
    commonsIo 'commons-io:commons-io:2.5'
configurations {
  compile.extendsFrom commonsIo

By putting all dependency declarations in a single place, we know where to look and we prevent multiple sub-projects from declaring the same dependency with different versions.

Furthermore, the sub-projects are now more declarative, specifying only what logical components they depend on, rather than all the details of how a component is built up from individual jar files. When there is a one-to-one correspondence, as in the commons IO example, that’s not such a big deal, but the difference is pronounced when working with components that are made up of multiple jars, like the Spring framework or Jetty.

Centralize dependency version declarations

The next step is to replace all the version numbers from the root project’s build.gradle file by properties defined in the root project’s

dependencies {
  commonsIo "commons-io:commons-io:$commonsIoVersion"

This practice allows you to reuse the version numbers for related dependencies. For instance, if you’re using the Spring framework, you may want to declare dependencies on spring-mvc and spring-jdbc with the same version number.

There is an additional advantage of this approach. Upgrading a dependency means updating, while adding a new dependency means updating build.gradle. This makes it easy to gauge from a commit feed what types of changes could have been made and thus to determine whether a closer inspection is warranted.

You can take this a step further and put the configurations and dependencies blocks in a separate file, e.g. dependencies.gradle.

And beyond…

Having all the dependencies declared in a single location is a stepping stone to more advanced supply chain management practices.

The centrally declared configurations give a good overview of all the components that you use in your product, the so-called Bill of Materials (BOM). You can use the above technique, or use the Gradle BOM plugin.

The BOM makes it easier to use a tool like OWASP DependencyCheck to check for publicly disclosed vulnerabilities in the dependencies that you use. At EMC, about 80% of vulnerabilities reported against our products are caused by issues in 3rd party components, so it makes sense to keep a security eye on dependencies.

A solid BOM also makes it easier to review licenses and their compliance requirements. If you can’t afford a tool like BlackDuck Protex, you can write something less advanced yourself with modest effort.

How To Process Java Annotations

One of the cool new features of Java 8 is the support for lambda expressions. Lambda expressions lean heavily on the FunctionalInterface annotation.

In this post, we’ll look at annotations and how to process them so you can implement your own cool features.


Annotations were added in Java 5. The Java language comes with some predefined annotations, but you can also define custom annotations.

Many frameworks and libraries make good use of custom annotations. JAX-RS, for instance, uses them to turn POJOs into REST resources.

Annotations can be processed at compile time or at runtime (or even both).

At runtime, you can use the reflection API. Each element of the Java language that can be annotated, like class or method, implements the AnnotatedElement interface. Note that an annotation is only available at runtime if it has the RUNTIME RetentionPolicy.

Compile-Time Annotation Processing

Java 5 came with the separate apt tool to process annotations, but since Java 6 this functionality is integrated into the compiler.

You can either call the compiler directly, e.g. from the command line, or indirectly, from your program.

In the former case, you specify the -processor option to javac, or you use the ServiceLoader framework by adding the file META-INF/services/javax.annotation.processing.Processor to your jar. The contents of this file should be a single line containing the fully qualified name of your processor class.

The ServiceLoader approach is especially convenient in an automated build, since all you have to do is put the annotation processor on the classpath during compilation, which build tools like Maven or Gradle will do for you.

Compile-Time Annotation Processing From Within Your Application

You can also use the compile-time tools to process annotations from within your running application.

Rather than calling javac directly, use the more convenient JavaCompiler interface. Either way, you’ll need to run your application with a JDK rather than just a JRE.

The JavaCompiler interface gives you programmatic access to the Java compiler. You can obtain an implementation of this interface using ToolProvider.getSystemJavaCompiler(). This method is sensitive to the JAVA_HOME environment variable.

The getTask() method of JavaCompiler allows you to add your annotation processor instances. This is the only way to control the construction of annotation processors; all other methods of invoking annotation processors require the processor to have a public no-arg constructor.

Annotation Processors

A processor must implement the Processor interface. Usually you will want to extend the AbstractProcessor base class rather than implement the interface from scratch.

Each annotation processor must indicate the types of annotations it is interested in through the getSupportedAnnotationTypes() method. You may return * to process all annotations.

The other important thing is to indicate which Java language version you support. Override the getSupportedSourceVersion() method and return one of the RELEASE_x constants.

With these methods implemented, your annotation processor is ready to get to work. The meat of the processor is in the process() method.

When process() returns true, the annotations processed are claimed by this processor, and will not be offered to other processors. Normally, you should play nice with other processors and return false.

Elements and TypeMirrors

The annotations and the Java elements they are present on are provided to your process() method as Element objects. You may want to process them using the Visitor pattern.

The most interesting types of elements are TypeElement for classes and interfaces (including annotations), ExecutableElement for methods, and VariableElement for fields.

Each Element points to a TypeMirror, which represents a type in the Java programming language. You can use the TypeMirror to walk the class relationships of the annotated code you’re processing, much like you would using reflection on the code running in the JVM.

Processing Rounds

Annotation processing happens in separate stages, called rounds. During each round, a processor gets a chance to process the annotations it is interested in.

The annotations to process and the elements they are present on are available via the RoundEnvironment parameter passed into the process() method.

If annotation processors generate new source or class files during a round, then the compiler will make those available for processing in the next round. This continues until no more new files are generated.

The last round contains no input, and is thus a good opportunity to release any resources the processor may have acquired.

Initializing and Configuring Processors

Annotation processors are initialized with a ProcessingEnvironment. This processing environment allows you to create new source or class files.

It also provides access to configuration in the form of options. Options are key-value pairs that you can supply on the command line to javac using the -A option. For this to work, you must return the options’ keys in the processor’s getSupportedOptions() method.

Finally, the processing environment provides some support routines (e.g. to get the JavaDoc for an element, or to get the direct super types of a type) that come in handy during processing.

Classpath Issues

To get the most accurate information during annotation processing, you must make sure that all imported classes are on the classpath, because classes that refer to types that are not available may have incomplete or altogether missing information.

When processing large numbers of annotated classes, this may lead to a problem on Windows systems where the command line becomes too large (> 8K). Even when you use the JavaCompiler interface, it still calls javac behind the scenes.

The Java compiler has a nice solution to this problem: you can use argument files that contain the arguments to javac. The name of the argument file is then supplied on the command line, preceded by @.

Unfortunately, the JavaCompiler.getTask() method doesn’t support argument files, so you’ll have to use the underlying run() method.

Remember that the getTask() approach is the only one that allows you to construct your annotation processors. If you must use argument files, then you have to use a public no-arg constructor.

If you’re in that situation, and you have multiple annotation processors that need to share a single instance of a class, you can’t pass that instance into the constructor, so you’ll be forced to use something like the Singleton pattern.


Annotations are an exciting technology that have lots of interesting applications. For example, I used them to extract the resources from a REST API into a resource model for further processing, like generating documentation.

I’m very interested to learn what you have used them for. Please leave a comment below.

How To Implement Input Validation For REST resources

rest-validationThe SaaS platform I’m working on has a RESTful interface that accepts XML payloads.

Implementing REST Resources

For a Java shop like us, it makes sense to use JAX-B to generate JavaBean classes from an XML Schema.

Working with XML (and JSON) payloads using JAX-B is very easy in a JAX-RS environment like Jersey:

public class OrdersResource {
  @Consumes({ "application/xml", "application/json" })
  public void place(Order order) {
    // Jersey marshalls the XML payload into the Order 
    // JavaBean, allowing us to write type-safe code 
    // using Order's getters and setters.
    int quantity = order.getQuantity();
    // ...

(Note that you shouldn’t use these generic media types, but that’s a discussion for another day.)

The remainder of this post assumes JAX-B, but its main point is valid for other technologies as well. Whatever you do, please don’t use XMLDecoder, since that is open to a host of vulnerabilities.

Securing REST Resources

Let’s suppose the order’s quantity is used for billing, and we want to prevent people from stealing our money by entering a negative amount.

We can do that with input validation, one of the most important tools in the AppSec toolkit. Let’s look at some ways to implement it.

Input Validation With XML Schema

xml-schemaWe could rely on XML Schema for validation, but XML Schema can only validate so much.

Validating individual properties will probably work fine, but things get hairy when we want to validate relations between properties. For maximum flexibility, we’d like to use Java to express constraints.

More importantly, schema validation is generally not a good idea in a REST service.

A major goal of REST is to decouple client and server so that they can evolve separately.

If we validate against a schema, then a new client that sends a new property would break against an old server that doesn’t understand the new property. It’s usually better to silently ignore properties you don’t understand.

JAX-B does this right, and also the other way around: properties that are not sent by an old client end up as null. Consequently, the new server must be careful to handle null values properly.

Input Validation With Bean Validation

bean-validationIf we can’t use schema validation, then what about using JSR 303 Bean Validation?

Jersey supports Bean Validation by adding the jersey-bean-validation jar to your classpath.

There is an unofficial Maven plugin to add Bean Validation annotations to the JAX-B generated classes, but I’d rather use something better supported and that works with Gradle.

So let’s turn things around. We’ll handcraft our JavaBean and generate the XML Schema from the bean for documentation:

@XmlRootElement(name = "order")
public class Order {
  public int quantity;
public class OrdersResource {
  @Consumes({ "application/xml", "application/json" })
  public void place(@Valid Order order) {
    // Jersey recognizes the @Valid annotation and
    // returns 400 when the JavaBean is not valid

Any attempt to POST an order with a non-positive quantity will now give a 400 Bad Request status.

Now suppose we want to allow clients to change their pending orders. We’d use PATCH or PUT to update individual order properties, like quantity:

public class OrdersResource {
  public Order update(@PathParam("id") String id, 
      @Min(1) @FormParam("quantity") int quantity) {
    // ...

We need to add the @Min annotation here too, which is duplication. To make this DRY, we can turn quantity into a class that is responsible for validation:

public class OrdersResource {
  public Order update(@PathParam("id") String id, 
      Quantity quantity) {
    // ...
@XmlRootElement(name = "order")
public class Order {
  public Quantity quantity;
public class Quantity {
  private int value;

  public Quantity() { }

  public Quantity(String value) {
    try {
    } catch (ValidationException e) {
      throw new IllegalArgumentException(e);

  public int getValue() {
    return value;

  public void setValue(int value) 
      throws ValidationException {
    if (value < 1) {
      throw new ValidationException(
          "Quantity value must be positive, but is: " 
          + value);
    this.value = value;

We need a public no-arg constructor for JAX-B to be able to unmarshall the payload into a JavaBean and another constructor that takes a String for the @FormParam to work.

setValue() throws javax.xml.bind.ValidationException so that JAX-B will stop unmarshalling. However, Jersey returns a 500 Internal Server Error when it sees an exception.

We can fix that by mapping validation exceptions onto 400 status codes using an exception mapper. While we’re at it, let’s do the same for IllegalArgumentException:

public class DefaultExceptionMapper 
    implements ExceptionMapper<Throwable> {

  public Response toResponse(Throwable exception) {
    Throwable badRequestException 
        = getBadRequestException(exception);
    if (badRequestException != null) {
      return Response.status(Status.BAD_REQUEST)
    if (exception instanceof WebApplicationException) {
      return ((WebApplicationException)exception)
    return Response.serverError()

  private Throwable getBadRequestException(
      Throwable exception) {
    if (exception instanceof ValidationException) {
      return exception;
    Throwable cause = exception.getCause();
    if (cause != null && cause != exception) {
      Throwable result = getBadRequestException(cause);
      if (result != null) {
        return result;
    if (exception instanceof IllegalArgumentException) {
      return exception;
    if (exception instanceof BadRequestException) {
      return exception;
    return null;


Input Validation By Domain Objects

dddEven though the approach outlined above will work quite well for many applications, it is fundamentally flawed.

At first sight, proponents of Domain-Driven Design (DDD) might like the idea of creating the Quantity class.

But the Order and Quantity classes do not model domain concepts; they model REST representations. This distinction may be subtle, but it is important.

DDD deals with domain concepts, while REST deals with representations of those concepts. Domain concepts are discovered, but representations are designed and are subject to all kinds of trade-offs.

For instance, a collection REST resource may use paging to prevent sending too much data over the wire. Another REST resource may combine several domain concepts to make the client-server protocol less chatty.

A REST resource may even have no corresponding domain concept at all. For example, a POST may return 202 Accepted and point to a REST resource that represents the progress of an asynchronous transaction.

ubiquitous-languageDomain objects need to capture the ubiquitous language as closely as possible, and must be free from trade-offs to make the functionality work.

When designing REST resources, on the other hand, one needs to make trade-offs to meet non-functional requirements like performance, scalability, and evolvability.

That’s why I don’t think an approach like RESTful Objects will work. (For similar reasons, I don’t believe in Naked Objects for the UI.)

Adding validation to the JavaBeans that are our resource representations means that those beans now have two reasons to change, which is a clear violation of the Single Responsibility Principle.

We get a much cleaner architecture when we use JAX-B JavaBeans only for our REST representations and create separate domain objects that handle validation.

Putting validation in domain objects is what Dan Bergh Johnsson refers to as Domain-Driven Security.

cave-artIn this approach, primitive types are replaced with value objects. (Some people even argue against using any Strings at all.)

At first it may seem overkill to create a whole new class to hold a single integer, but I urge you to give it a try. You may find that getting rid of primitive obsession provides value even beyond validation.

What do you think?

How do you handle input validation in your RESTful services? What do you think of Domain-Driven Security? Please leave a comment.

The Lazy Developer’s Way to an Up-To-Date Libraries List

groovyLast time I shared some tips on how to use libraries well. I now want to delve deeper into one of those: Know What Libraries You Use.

Last week I set out to create such a list of embedded components for our product. This is a requirement for our Security Development Lifecycle (SDL).

However, it’s not a fun task. As a developer, I want to write code, not update documents! So I turned to my friends Gradle and Groovy, with a little help from Jenkins and Confluence.

Gradle Dependencies

We use Gradle to build our product, and Gradle maintains the dependencies we have on third-party components.

Our build defines a list of names of configurations for embedded components, copyBundleConfigurations, for copying those to the distribution directory. From there, I get to the external dependencies using Groovy’s collection methods:

def externalDependencies() {
  copyBundleConfigurations.collectMany { 
  }.findAll {
    !(it instanceof ProjectDependency) && &&

Adding Required Information

However, Gradle dependencies don’t contain all the required information.

For instance, we need the license under which the library is distributed, so that we can ask the Legal department permission for using it.

So I added a simple XML file to hold the additional info. Combining that information with the dependencies that Gradle maintains is easy using Groovy’s XML support:

ext.embeddedComponentsInfo = 'embeddedComponents.xml'

def externalDependencyInfos() {
  def result = new TreeMap()
  def componentInfo = new XmlSlurper()
  externalDependencies().each { dependency ->
    def info = componentInfo.component.find { == "$$" &&
    if (!info.isEmpty()) {
      def component = [
        'friendlyName': info.friendlyName.text(),
        'version': dependency.version,
        'latestVersion': info.latestVersion.text(),
        'license': info.license.text(),
        'licenseUrl': info.licenseUrl.text(),
        'comment': info.comment.text()
      result.put component.friendlyName, component

I then created a Gradle task to write the information to an HTML file. Our Jenkins build executes this task, so that we always have an up-to-date list. I used Confluence’s html-include macro to include the HTML file in our Wiki.

Now our Wiki is always up-to-date.

Automatically Looking Up Missing Information

The next problem was to populate the XML file with additional information.

Had we had this file from the start, adding that information manually would not have been a big deal. In our case, we already had over a hundred dependencies, so automation was in order.

First I identified the components that miss the required information:

def missingExternalDependencies() {
  def componentInfo = new XmlSlurper()
  externalDependencies().findAll { dependency ->
    componentInfo.component.find { == "$$" && 
  }.collect {

Next, I wanted to automatically look up the missing information and add it to the XML file (using Groovy’s MarkupBuilder). In case the required information can’t be found, the build should fail:

project.afterEvaluate {
  def missingComponents = missingExternalDependencies()
  if (!missingComponents.isEmpty()) {
    def manualComponents = []
    def writer = new StringWriter() 
    def xml = new MarkupBuilder(writer)
    xml.expandEmptyElements = true
    println 'Looking up information on new dependencies:'
    xml.components {
      externalDependencyInfos().each { existingComponent ->
        component { 
      missingComponents.each { missingComponent ->
        def lookedUpComponent = collectInfo(missingComponent)
        component {
        if (!lookedUpComponent.friendlyName || 
            !lookedUpComponent.latestVersion || 
            !lookedUpComponent.license) {
          println '    => Please enter information manually'
    def embeddedComponentsFile = 
    embeddedComponentsFile.text = writer.toString()
    if (!manualComponents.isEmpty()) {
      throw new GradleException('Missing library information')

Anyone who adds a dependency in the future is now forced to add the required information.

So all that is left to implement is the collectInfo() method.

There are two primary sources that I used to look up the required information: the SpringSource Enterprise Bundle Repository holds OSGi bundle versions of common libraries, while Maven Central holds regular jars.

Extracting information from those sources is a matter of downloading and parsing XML and HTML files. This is easy enough with Groovy’s String.toURL() and URL.eachLine() methods and support for regular expressions.


All of this took me a couple of days to build, but I feel that the investment is well worth it, since I no longer have to worry about the list of used libraries being out of date.

How do you maintain a list of used libraries? Please let me know in the comments.

Behavior-Driven Development (BDD) with JBehave, Gradle, and Jenkins

Behavior-Driven Development (BDD) is a collaborative process where the Product Owner, developers, and testers cooperate to deliver software that brings value to the business.

BDD is the logical next step up from Test-Driven Development (TDD).

Behavior-Driven Development

In essence, BDD is a way to deliver requirements. But not just any requirements, executable ones! With BDD, you write scenarios in a format that can be run against the software to ascertain whether the software behaves as desired.


Scenarios are written in Given, When, Then format, also known as Gherkin:

Given the ATM has $250
And my balance is $200
When I withdraw $150
Then the ATM has $100
And my balance is $50

Given indicates the initial context, When indicates the occurrence of an interesting event, and Then asserts an expected outcome. And may be used to in place of a repeating keyword, to make the scenario more readable.

Given/When/Then is a very powerful idiom, that allows for virtually any requirement to be described. Scenarios in this format are also easily parsed, so that we can automatically run them.

BDD scenarios are great for developers, since they provide quick and unequivocal feedback about whether the story is done. Not only the main success scenario, but also alternate and exception scenarios can be provided, as can abuse cases. The latter requires that the Product Owner not only collaborates with testers and developers, but also with security specialists. The payoff is that it becomes easier to manage security requirements.

Even though BDD is really about the collaborative process and not about tools, I’m going to focus on tools for the remainder of this post. Please keep in mind that tools can never save you, while communication and collaboration can. With that caveat out of the way, let’s get started on implementing BDD with some open source tools.


JBehave is a BDD tool for Java. It parses the scenarios from story files, maps them to Java code, runs them via JUnit tests, and generates reports.


JBehave has a plug-in for Eclipse that makes writing stories easier with features such as syntax highlighting/checking, step completion, and navigation to the step implementation.


Here’s how we run our stories using JUnit:

@UsingEmbedder(embedder = Embedder.class, generateViewAfterStories = true,
    ignoreFailureInStories = true, ignoreFailureInView = false, 
    verboseFailures = true)
@UsingSteps(instances = { NgisRestSteps.class })
public class StoriesTest extends JUnitStories {

  protected List<String> storyPaths() {
    return new StoryFinder().findPaths(
        Arrays.asList(getStoryFilter(storyPaths)), null);

  private String getStoryFilter(String storyPaths) {
    if (storyPaths == null) {
      return "*.story";
    if (storyPaths.endsWith(".story")) {
      return storyPaths;
    return storyPaths + ".story";

  private List<String> specifiedStoryPaths(String storyPaths) {
    List<String> result = new ArrayList<String>();
    URI cwd = new File("src/test/resources").toURI();
    for (String storyPath : storyPaths.split(File.pathSeparator)) {
      File storyFile = new File(storyPath);
      if (!storyFile.exists()) {
        throw new IllegalArgumentException("Story file not found: " 
          + storyPath);
    return result;

  public Configuration configuration() {
    return super.configuration()
        .useStoryReporterBuilder(new StoryReporterBuilder()
            .withFormats(Format.XML, Format.STATS, Format.CONSOLE)
        .usePendingStepStrategy(new FailingUponPendingStep())
        .useFailureStrategy(new SilentlyAbsorbingFailure());


This uses JUnit 4’s @RunWith annotation to indicate the class that will run the test. The AnnotatedEmbedderRunner is a JUnit Runner that JBehave provides. It looks for the @UsingEmbedder annotation to determine how to run the stories:

  • generateViewAfterStories instructs JBehave to create a test report after running the stories
  • ignoreFailureInStories prevents JBehave from throwing an exception when a story fails. This is essential for the integration with Jenkins, as we’ll see below

The @UsingSteps annotation links the steps in the scenarios to Java code. More on that below. You can list more than one class.

Our test class re-uses the JUnitStories class from JBehave that makes it easy to run multiple stories. We only have to implement two methods: storyPaths() and configuration().

The storyPaths() method tells JBehave where to find the stories to run. Our version is a little bit complicated because we want to be able to run tests from both our IDE and from the command line and because we want to be able to run either all stories or a specific sub-set.

We use the system property bdd.stories to indicate which stories to run. This includes support for wildcards. Our naming convention requires that the story file names start with the persona, so we can easily run all stories for a single persona using something like -Dbdd.stories=wanda_*.

The configuration() method tells JBehave how to run stories and report on them. We need output in XML for further processing in Jenkins, as we’ll see below.

One thing of interest is the location of the reports. JBehave supports Maven, which is fine, but they assume that everybody follows Maven conventions, which is really not. The output goes into a directory called target by default, but we can override that by specifying a path relative to the target directory. We use Gradle instead of Maven, and Gradle’s temporary files go into the build directory, not target. More on Gradle below.


Now we can run our stories, but they will fail. We need to tell JBehave how to map the Given/When/Then steps in the scenarios to Java code. The Steps classes determine what the vocabulary is that can be used in the scenarios. As such, they define a Domain Specific Language (DSL) for acceptance testing our application.

Our application has a RESTful interface, so we wrote a generic REST DSL. However, due to the HATEOAS constraint in REST, a client needs a lot of calls to discover the URIs that it should use. Writing scenarios gets pretty boring and repetitive that way, so we added an application-specific DSL on top of the REST DSL. This allows us to write scenarios in terms the Product Owner understands.

Layering the application-specific steps on top of generic REST steps has some advantages:

  • It’s easy to implement new application-specific DSL, since they only need to call the REST-specific DSL
  • The REST-specific DSL can be shared with other projects


With the Steps in place, we can run our stories from our favorite IDE. That works great for developers, but can’t be used for Continuous Integration (CI).

Our CI server runs a headless build, so we need to be able to run the BDD scenarios from the command line. We automate our build with Gradle and Gradle can already run JUnit tests. However, our build is a multi-project build. We don’t want to run our BDD scenarios until all projects are built, a distribution is created, and the application is started.

So first off, we disable running tests on the project that contains the BDD stories:

test {
  onlyIf { false } // We need a running server

Next, we create another task that can be run after we start our application:

task acceptStories(type: Test) {
  ignoreFailures = true
  doFirst {
    // Need 'target' directory on *nix systems to get any output

    def filter = System.getProperty('bdd.stories') 
    if (filter == null) {
      filter = '*'
    def stories = sourceSets.test.resources.matching { 
      it.include filter
    systemProperty('bdd.stories', stories)

Here we see the power of Gradle. We define a new task of type Test, so that it already can run JUnit tests. Next, we configure that task using a little Groovy script.

First, we must make sure the target directory exists. We don’t need or even want it, but without it, JBehave doesn’t work properly on *nix systems. I guess that’s a little Maven-ism 😦

Next, we add support for running a sub-set of the stories, again using the bdd.stories system property. Our story files are located in src/test/resources, so that we can easily get access to them using the standard Gradle test source set. We then set the system property bdd.stories for the JVM that runs the tests.


So now we can run our BDD scenarios from both our IDE and the command line. The next step is to integrate them into our CI build.

We could just archive the JBehave reports as artifacts, but, to be honest, the reports that JBehave generates aren’t all that great. Fortunately, the JBehave team also maintains a plug-in for the Jenkins CI server. This plug-in requires prior installation of the xUnit plug-in.

After installation of the xUnit and JBehave plug-ins into jenkins, we can configure our Jenkins job to use the JBehave plug-in. First, add an xUnit post-build action. Then, select the JBehave test report.

With this configuration, the output from running JBehave on our BDD stories looks just like that for regular unit tests:

Note that the yellow part in the graph indicates pending steps. Those are used in the BDD scenarios, but have no counterpart in the Java Steps classes. Pending steps are shown in the Skip column in the test results:

Notice how the JBehave Jenkins plug-in translates stories to tests and scenarios to test methods. This makes it easy to spot which scenarios require more work.

Although the JBehave plug-in works quite well, there are two things that could be improved:

  • The output from the tests is not shown. This makes it hard to figure out why a scenario failed. We therefore also archive the JUnit test report
  • If you configure ignoreFailureInStories to be false, JBehave throws an exception on a failure, which truncates the XML output. The JBehave Jenkins plug-in can then no longer parse the XML (since it’s not well formed), and fails entirely, leaving you without test results

All in all these are minor inconveniences, and we ‘re very happy with our automated BDD scenarios.

Root Cause Analysis

I’ve moved on to a new project recently. It’s quite different from the previous one. Before I worked on a monolythic web application, now we’re using OSGi. As a result, our project consists of a lot of sub-projects (OSGi bundles) which makes it very inconvenient to use Ant. So we’ve switched to Gradle. Our company has also standardized on Perforce, where we used Subversion before.

To summarize, a lot has changed and I’m not really up to speed yet. This is evidenced by the fact that I broke our build 4 times in 5 days. As an aspiring software craftsman, I feel really bad about that. So why did this happen so often? And can I do anything to prevent it from happening again? Enter Root Cause Analysis.

Root cause analysis is performed to not just treat the symptoms, but cure the disease:

The key to effective problem solving is to first make sure you understand the problem that you are
trying to solve – why it needs to be solved, how you will know when you’ve solved it, and what the
root cause is.
Often symptoms show up in one place while the actual cause of the problem is somewhere
completely different. If you just “solve” the symptom without digging deeper it is highly likely that
problem will just reappear later in a different shape.

Henrik Kniberg has written about one way of doing root cause analysis: using Cause-Effect diagrams. Using this method, I ended up with the following:

Build Failure Cause Effect Diagram

I started out with the problem: Build Failure, in the orange rectangle. I then repeatedly asked myself why this is a bad thing and added the effects in the red rectangles. Just repeat this until you find something that conflicts with your goal. Finally, I repeatedly added causes in blue rectangles. You can stop when you’ve found something you can fix, but in general it’s good to keep asking a bit deeper than feels comfortable. This is where the Five Whys technique comes in handy.

As you can see, my main problem is impatience. For those who know me, that won’t come as a surprise 😉 However, in this case this personal flaw of mine gets in the way of my goal of making customers happy.

With the causes identified, it’s time to think up some countermeasures. Pick some causes that you can fix, and think of a way to treat them. The ones I’m going to work on are marked with a star in the figure above:

  • Use a checklist when submitting code to the source code repository. This will prevent me from making silly mistakes, such as forgetting to add a new file
  • Take the time to learn the tools better, in particular Gradle and Groovy
  • In general, try to be more patient

The last one is the hard one, of course. Wish me luck!

Update 2010-08-01: I have created a checklist with things to consider before submitting code to our Perforce repository and used this checklist all week. I broke the build twice this week, both times because of special circumstances that were not accounted for on the checklist. So I think I’m improving, but I’m not there yet.