Java

Cucumber-JVM Global Hook Workarounds

Almost all BDD automation frameworks have some sort of hooks that run before and after scenarios. However, not all frameworks have global hooks that run once at the beginning or end of a suite of scenarios – and Cucumber-JVM is one of these unlucky few. Cucumber-JVM GitHub Issue #515, which seeks to add @BeforeAll and @AfterAll hooks, has been open and active since 2013, but it looks unclear if the issue will ever be resolved. Thankfully, there are some workarounds to effect the same behavior as global hooks.

Workaround #1: Don’t Do It

From a purist’s perspective, each scenario (or test) should be completely independent, meaning it should not share parts with any other tests. Independence provides the following benefits:

  • Safety between tests
  • Consistency across tests
  • The ability to run any tests individually, in any order, or in parallel
  • More sensible, understandable tests

If not handled properly, global hooks can be dangerous because they make tests interdependent. Changes or failures in one test may cascade into others. Global test data would waste memory for tests that don’t use it. Furthermore, the fact that Issue #515 has been open for years indicates the difficulty of properly implementing global hooks.

However, the main cost of independence is runtime. Independent tests often repeat similar setup and cleanup routines. Even a few extra seconds per test can add up tremendously. Google Guava, for example, has over 286,000 tests – adding one second to each test would amount to nearly 80 hours! Performance becomes especially critical for continuous integration, in which wasted time means either delivery delays or coverage gaps. Certain operations like preparing a database or fetching authentication tokens may be pragmatic candidates for global hooks.

The best strategy is to use global hooks only when necessary for time-intensive setup that can be shared safely. Any shared test data should be immutable. Always question the need for global hooks. Most tests probably won’t need them.

Workaround #2: Static Variables

A basic hack for global hooks is actually provided in Issue #515. A static Boolean flag can indicate when the @Before hook has run more than once because it isn’t “reset” when a new scenario re-instantiates the step definition classes. The runtime shutdown hook will be called once all tests are done and the program exits. (Note that a static flag cannot be used in an @After hook due to the halting problem.) The example from the issue is shamelessly copied below:

public class GlobalHooks {
    private static boolean dunit = false;

    @Before
    public void beforeAll() {
        if(!dunit) {
            Runtime.getRuntime().addShutdownHook(afterAllThread);
            // do the beforeAll stuff...
            dunit = true;
        }
    }
}

Workaround #3: Singleton Caching

The basic hack is useful for simple setup and cleanup routines, but it becomes inelegant when objects must be shared by scenarios. Rather than polluting the class with static members, a singleton can cache test data between scenarios, and global setup logic may be put into the singleton’s constructor. Furthermore, if the singleton uses lazy initialization, then @Before hooks may not be needed at all. A “lazy” singleton will not be instantiated until the first time its getInstance method is called, meaning it will be skipped if the scenarios do not need them. This is a huge advantage when selectively running scenarios by name, tag, or feature. (Please refer to the previous post, Static or Singleton, for a deeper explanation of the singleton pattern.)

Consider scenarios that must generate authentication tokens (like OAuth) for API testing. A singleton “token holder” could cache tokens for usernames, rather than doing the authorization dance for every scenario. The snippet below shows how such a singleton could be called within a @When step definition with no @Before method.

public class ExampleSteps {
    ...
    @When("^some API is called$")
    public void whenSomeApiIsCalled() {
        // Get the token from the singleton cache lazily
        String token = TokenHolder.getInstance().getToken("user", "pass");
        // Use the token to call some API (method not shown)
        callSomeApi(token);
    }
    ...
}

And the singleton class could be defined like this:

public class TokenHolder {
    private static volatile TokenHolder instance = null;
    private HashMap<String, String> tokens;

    private TokenHolder() {
        tokens = new HashMap<String, String>();
    }

    public static TokenHolder getInstance() {
        // Lazy and thread-safe
        if (instance == null) {
            synchronized(TokenHolder.class) {
                if (instance == null) {
                    instance = new TokenHolder();
                }
            }
        }

        return instance;
    }
    
    public String getToken(String username, String password) {
        // This check could be extended to handle token expiration
        if (!tokens.containsKey(username)) {
            // Request a fresh authentication token (method not shown)
            String token = requestToken(username, password);
            // Cache the token for later
            tokens.put(username, token);
        }
        
        return tokens.get(username);
    }
    
    ...
}

Workaround #4: JUnit Class Annotations

Another workaround mentioned in Issue #515 and elsewhere is to use JUnit‘s @BeforeClass and @AfterClass annotations in the runner class, like this:

@RunWith(Cucumber.class)
@Cucumber.Options(format = {
    "html:target/cucumber-html-report",
    "json-pretty:target/cucumber-json-report.json"})
public class RunCukesTest {

    @BeforeClass
    public static void setup() {
        System.out.println("Ran the before");
    }

    @AfterClass
    public static void teardown() {
        System.out.println("Ran the after");
    }
}

While @BeforeClass and @AfterClass may look like the cleanest solution at first, they are not very practical to use. They work only when Cucumber-JVM is set to use the JUnit runner. Other runners, like TestNG, the command line runner, and special IDE runners, won’t pick up these hooks. Their methods must also be are static and would need static variables or singletons to share data anyway. Therefore, I personally discourage using these annotations in Cucumber-JVM.

What About Dependency Injection?

Dependency injection is a marvelous technique. As defined by Wikipedia:

In software engineering, dependency injection is a technique whereby one object supplies the dependencies of another object. A dependency is an object that can be used (a service). An injection is the passing of a dependency to a dependent object (a client) that would use it. The service is made part of the client’s state. Passing the service to the client, rather than allowing a client to build or find the service, is the fundamental requirement of the pattern.

Dependency injection can be a powerful alternative to singletons because DI provides finer control over the scope of objects. However, Cucumber-JVM’s dependency injection cannot be applied with global hooks because dependency objects, like step definition objects, are constructed and destroyed for each scenario.

Comparison Table

Ultimately, the best approach for global hooks in Cucumber-JVM is the one that best fits the tests’ needs. Below is a table to make workaround comparisons easier.

Workaround Pros Cons
Don’t Do It Scenarios are completely independent. No complicated or risky workarounds. Repeated setup and cleanup procedures may add significant execution time.
Static Variables Simple yet effective implementation. May need many static variables to share test data.
Singleton Caching Abstracts test data and setup procedures. Easily handles lazy initialization and evaluation. May not need a @Before hook. More complicated design.
JUnit Class Annotations Clean look for basic setup and cleanup routines. May be used only with the JUnit runner. Requires static variables or singletons to share test data anyway.

BDD 101: Frameworks

Every major programming language has a BDD automation framework. Some even have multiple choices. Building upon the structural basics from the previous post, this post provides a survey of the major frameworks available today. Since I cannot possibly cover every BDD framework in depth in this 101 series, my goal is to empower you, the reader, to pick the best framework for your needs. Each framework has support documentation online justifying its unique goodness and detailing how to use it, and I would prefer not to duplicate documentation. Use this post primarily as a reference.

Major Frameworks

Most BDD frameworks are Cucumber versions, JBehave derivatives inspired by Dan North, or non-Gherkin spec runners. Some put behavior scenarios into separate files, while others put them directly into the source code.

C# and Microsoft .NET

SpecFlow is arguably the most popular BDD framework for Microsoft .NET languages. Its tagline is “Cucumber for .NET” – thus fully compliant with Gherkin. The basic package is free and open source, but SpecFlow also sells licenses for SpecFlow+ extensions. The free version requires a unit test runner like MsTest, NUnit, or xUnit.net in order to run scenarios. This makes SpecFlow flexible but also feels jury-rigged and inelegant. The licensed version provides a slick runner named SpecFlow+ Runner (which is BDD-friendly) and a Microsoft Excel integration tool named SpecFlow+ Excel. Microsoft Visual Studio has extensions for SpecFlow to make development easier.

There are plenty of other BDD frameworks for C# and .NET, too. xBehave.net is an alternative that pairs nicely with xUnit.net. A major difference of xBehave.net is that scenario steps are written directly in the code, instead of in separate text (feature) files. LightBDD bills itself as being more lightweight than other frameworks and basically does some tricks with partial classes to make the code more readable. NSpec is similar to RSpec and Mocha and uses lambda expressions heavily. Concordion offers some interesting ways to write specs, too. NBehave is a JBehave descendant, but the project appears to be dead without any updates since 2014.

Java and JVM Languages

The main Java rivalry is between Cucumber-JVM and JBehave. Cucumber-JVM is the official Cucumber version for Java and other JVM languages (Groovy, Scala, Clojure, etc.). It is fully compliant with Gherkin and generates beautiful reports. The Cucumber-JVM driver can be customized, as well. JBehave is one of the first and foremost BDD frameworks available. It was originally developed by Dan North, the “father of BDD.” However, JBehave is missing key Gherkin features like backgrounds, doc strings, and tags. It was also a pure-Java implementation before Cucumber-JVM existed. Both frameworks are widely used, have plugins for major IDEs, and distribute Maven packages. This popular but older article compares the two in slight favor of JBehave, but I think Cucumber-JVM is better, given its features and support.

Java also has a number of other BDD frameworks. JGiven uses a fluent API to spell out scenarios, and pretty HTML reports print the scenarios with the results. It is fairly clean and concise. Spock and JDave are spec frameworks, but JDave has been inactive for years. Scalatest for Scala also has spec-oriented features. Concordion also provides a Java implementation.

JavaScript

Almost all JavaScript BDD frameworks run on Node.js. Mocha is a general-purpose test framework that integrates English-y phrases into spec-like code. Jasmine is like Mocha but has less of a learning curve. Cucumber provides Cucumber.js for Gherkin-compliant happiness. Yadda is Gherkin-like but with a more flexible syntax. Vows provides a different way to approach behavior using more formalized phrase partitions for a unique form of reusability. Comparisons are posted here, here, here, and here. The Cucumber blog argues that Cucumber.js is best due to its focus on good communication through plain language steps, whereas other JavaScript BDD frameworks are more code-y.

PHP

The two major BDD frameworks for PHP are Behat and Codeception. Behat is the official Cucumber version for PHP, and as such is seen as the more “pure” BDD framework. Codeception is more programmer-focused and can handle other styles of testing. There are plenty of articles comparing the two – here, here, and here (although the last one seems out of date). Both seem like good choices, but Codeception seems more flexible.

Python

Python has a plethora of test frameworks, and many are BDD. behave and lettuce are probably the two most popular players. Feature comparison is analogous to Cucumber-JVM versus JBehave, respectively: behave is fully Gherkin compliant, while lettuce lacks a few language elements. Both have plugins for major IDEs. radish is another framework that extends the Gherkin language to include scenario loops, scenario preconditions, and variables. All three put scenarios into separate feature files. They all also implement step definitions as functions instead of classes, which not only makes steps feel simpler and more independent, but also avoids unnecessary object construction.

Other Python frameworks exist as well. pyspecs is a spec-oriented framework. pytest-bdd adds some Gherkin features to the popular pytest library. Freshen was a BDD plugin for Nose, but both Freshen and Nose are discontinued projects.

Ruby

Cucumber, the gold standard for BDD frameworks, was first implemented in Ruby. Cucumber maintains the official Gherkin language standard, and all Cucumber versions are inspired by the original Ruby version. Spinach bills itself as an enhancement to Cucumber by encapsulating steps better. RSpec is a spec-oriented framework that does not use Gherkin.

Which One is Best?

There is no right answer – the best BDD framework is the one that best fits your needs. However, there are a few points to consider when weighing your options:

  • What programming language should I use for test automation?
  • Is it a popular framework that many others use?
  • Is the framework actively supported?
  • Is the spec language compliant with Gherkin?
  • What type of testing will you do with the framework?
  • What are the limitations as compared to other frameworks?

Frameworks that separate scenario text from implementation code are best for shift-left testing. Frameworks that put scenario text directly into the source code are better for white box testing, but they may look confusing to less experienced programmers.

Personally, my favorites are Cucumber-JVM and behave. At my present job, I use SpecFlow and prefer it above the other .NET frameworks. I’d love to learn more about radish, and I’d love to try JGiven for unit tests. For skill transferability, I recommend Gherkin compliance, as well.

Reference Table

The table below categorizes BDD frameworks by language and type for quick reference. It also includes frameworks in languages not described above. Recommended frameworks are denoted with an asterisk (*). Inactive projects are denoted with an X (x).

Language Framework Type
C Catch In-line Spec
C++ Igloo In-line Spec
C# and .NET Concordion
LightBDD
NBehave x
NSpec
SpecFlow *
xBehave.net
In-line Spec
In-line Gherkin
Separated semi-Gherkin
In-line Spec
Separated Gherkin
In-line Gherkin
Golang Ginkgo In-line Spec
Java and JVM Cucumber-JVM *
JBehave
JDave x
JGiven *
Scalatest
Spock
Separated Gherkin
Separated semi-Gherkin
In-line Spec
In-line Gherkin
In-line Spec
In-line Spec
JavaScript Cucumber.js *
Jasmine
Mocha
Vows
Yadda
Separated Gherkin
In-line Spec
In-line Spec
In-line Spec
Separated semi-Gherkin
Perl Test::BDD::Cucumber Separated Gherkin
PHP Behat
Codeception *
Separated Gherkin
Separated or In-line
Python behave *
freshen x
lettuce
pyspecs
pytest-bdd
radish *
Separated Gherkin
Separated Gherkin
Separated semi-Gherkin
In-line Spec
Separated semi-Gherkin
Separated Gherkin-plus
Ruby Cucumber *
RSpec
Spinach
Separated Gherkin
In-line Spec
Separated Gherkin
Swift / Objective C Quick In-line Spec

 

The Best Programming Language for Test Automation

Which programming languages are best for writing test automation? There are several choices – just look at this list on Wikipedia and this cool decision graphs for choosing languages. While this topic can quickly devolve into a spat over personal tastes, I do believe there are objective reasons for why some languages are better for automating test cases than others.

Dividing Test Layers

First of all, unit tests should always be written in the same language as the product under test. Otherwise, they would definitionally no longer be unit tests! Unit tests are white box and need direct access to the product source code. This allows them to cover functions, methods, and classes.

The question at hand pertains more to higher-layer functional tests. These tests fall into many (potentially overlapping) categories: integration, end-to-end, system, acceptance, regression, and even performance. Since they are all typically black box, higher-layer tests do not necessarily need to be written in the same language as the product under test.

My Choices

Personally, I think Python and Java are today’s best languages for test automation. Python, in particular, is wonderful because its conciseness lets the programmer expressively capture the essence of the test case. Java has a rich platform of tools and packages, and continuous integration with Java is easy with Maven/Gradle/ANT and Jenkins. I’ve heard that Ruby is another good choice for reasons similar to Python, but I have not used it myself. JavaScript is good for pure web app testing (à la Protractor) but not so good for general purposes.

On the other hand, languages like C, C++, C#, and Perl are less suitable for test automation. C and C++ are very low-level and lack robust frameworks. Although C# as a language is similar to Java, it lives in the Microsoft bubble: .NET development tools are not as friendly or as free, and command line operations are painful. Perl simply does not provide the consistency and structure for scalable and self-documenting code. Purely functional languages like LISP and Haskell are also poor choices for test automation because they do not translate well from test case procedures. They may be useful, however, for some lower-level data testing.

8 Criteria for Evaluation

There are eight major points to consider when evaluating any language for automation. These criteria specifically assess the language from a perspective of purity and usability, not necessarily from a perspective of immediate project needs.

  1. Usability.  A good automation language is fairly high-level and should handle rote tasks like memory management. Lower learning curves are also preferable. Development speed is also important for deadlines.
  2. Elegance. The process of translating test case procedures into code must be easy and clear. Test code should also be concise and self-documenting for maintainability.
  3. Available Test Frameworks. Frameworks provide basic needs such as assertions, setup/cleanup, logging, and reporting. Examples include Cucumber and xUnit.
  4. Available Packages. It is better to use off-the-shelf packages for common operations, such as web drivers (Selenium), HTTP requests, and SSH.
  5. Powerful Command Line. A good CLI makes launching tests easy. This is critical for continuous integration, where tests cannot be launched manually.
  6. Easy Build Integration. Build automation should launch tests and report results. Difficult integration is a DevOps nightmare.
  7. IDE Support. Because Notepad and vim just don’t cut it for big projects.
  8. Industry Adoption. Support is good. If the language remains popular, then frameworks and packages will be maintained well.

Below, I rated each point for a few popular languages:

Python Java C# C/C++ Perl
Usability  awesome  good  good  terrible  poor
Elegance  awesome  good  good  poor  poor
Available Test Frameworks  awesome  awesome  good  okay  poor
Available Packages  awesome  awesome  good  good  good
Powerful Command Line  awesome  good  terrible  poor  okay
Easy Build Integration  good  awesome  poor  poor  poor
IDE Support  good  awesome  okay  okay  terrible
Industry Adoption  awesome  awesome  good  terrible  terrible

Conclusion

I won’t shy away from my preference for Python and Java, but I recognize that they may not be the right choice for all situations. For example, we use C# at my current job because our app is written in C# and management wants developers and QA to be on the same page.

Now, a truly nifty idea would be to create a domain-specific language for test automation, but that must be a topic for another post.