automation

Software Testing Lessons from Luigi’s Mansion

How can lessons from Luigi’s Mansion apply to software testing and automation?

Luigi’s Mansion is a popular Nintendo video game series. It’s basically Ghostbusters in the Super Mario universe: Luigi must use a special vacuum cleaner to rid haunted mansions of the ghosts within. Along the way, Luigi also solves puzzles, collects money, and even rescues a few friends. I played the original Luigi’s Mansion game for the Nintendo GameCube when I was a teenager, and I recently beat the sequel, Luigi’s Mansion: Dark Moon, for the Nintendo 3DS. They were both quite fun! And there are some lessons we can apply from Luigi’s Mansion to software testing and automation.

#1: Exploratory Testing is Good

The mansions are huge – Luigi must explore every nook and cranny (often in the dark) to spook ghosts out of their hiding places. There are also secrets and treasure hiding in plain sight everywhere. Players can easily miss ghosts and gold alike if they don’t take their time to explore the mansions thoroughly. The same is true with testing: engineers can easily miss bugs if they overlook details. Exploratory testing lets engineers freely explore the product under test to uncover quality issues that wouldn’t turn up through rote test procedures.

#2: Expect the Unexpected

Ghosts can pop out from anywhere to scare Luigi. They also can create quite a mess of the mansion – blocking rooms, stealing items, and even locking people into paintings! Software testing is full of unexpected problems, too. Bugs happen. Environments go down. Network connections break. Even test automation code can have bugs. Engineers must be prepared for any emergency regardless of origin. Software development and testing is about solving problems, not about blame-games.

#3: Don’t Give Up!

Getting stuck somewhere in the mansion can be frustrating. Some puzzles are small, while others may span multiple rooms. Sometimes, a player may need to backtrack through every room and vacuum every square inch to uncover a new hint. Determination nevertheless pays off when puzzles get solved. Software engineers must likewise never give up. Failures can be incredibly complex to identify, reproduce, and resolve. Test automation can become its own nightmare, too. However, there is always a solution for those tenacious (or even hardheaded) enough to find it.

 

Want to see what software testing lessons can be learned from other games? Check out Gotta Catch ’em All! for Pokémon!

Gherkin Syntax Highlighting in Chrome

Google Chrome is one of the most popular web browsers around. Recently, I discovered that Chrome can edit and display Gherkin feature files. The Chrome Web Store has two useful extensions for Gherkin: Tidy Gherkin and Pretty Gherkin, both developed by Martin Roddam. Together, these two extensions provide a convenient, lightweight way to handle feature files.

Tidy Gherkin

Tidy Gherkin is a Chrome app for editing and formatting feature files. Once it is installed, it can be reached from the Chrome Apps page (chrome://apps/). The editor appears in a separate window. Gherkin text is automatically colored as it is typed. The bottom preview pane automatically formats each line, and clicking the “TIDY!” button in the upper-left corner will format the user-entered text area as well. Feature files can be saved and opened like a regular text editor. Templates for Feature, Scenario, and Scenario Outline sections may be inserted, as well as tables, rows, and columns.

Another really nice feature of Tidy Gherkin is that the preview pane automatically generates step definition stubs for Java, Ruby, and JavaScript! The step def code is compatible with the Cucumber test frameworks. (The Java code uses the traditional step def format, not the Java 8 lambdas.) This feature is useful if you aren’t already using an IDE for automation development.

Tidy Gherkin has pros and cons when compared to other editors like Notepad++ and Atom. The main advantages are automatic formatting and step definition generation – features typically seen only in IDEs. It’s also convenient for users who already use Chrome, and it’s cross-platform. However, it lacks richer text editing features offered by other editors, it’s not extendable, and the step def gen feature may not be useful to all users. It also requires a bit of navigation to open files, whereas other editors may be a simple right-click away. Overall, Tidy Gherkin is nevertheless a nifty, niche editor.

This slideshow requires JavaScript.

Pretty Gherkin

Pretty Gherkin is a Chrome extension for viewing Gherkin feature files through the browser with syntax highlighting. After installing it, make sure to enable the “Allow access to the file URLs” option on the Chrome Extensions page (chrome://extensions/). Then, whenever Chrome opens a feature file, it should display pretty text. For example, try the GoogleSearch.feature file from my Cucumber-JVM example project, cucumber-jvm-java-example. Unfortunately, though, I could not get Chrome to display local feature files – every time I would try to open one, Chrome would simply download it. Nevertheless, Pretty Gherkin seems to work for online SCM sites like GitHub and BitBucket.

Since Pretty Gherkin is simply a display tool, it can’t really be compared to other editors. I’d recommend Pretty Gherkin to Chrome users who often read feature files from online code repositories.

This slideshow requires JavaScript.

 

Be sure to check out other Gherkin editors, too!

Unpredictable Test Data

Test data is a necessary evil for testing and automation. It is necessary because tests simply can’t run without test case values, configuration data, and ready state (as detailed in BDD 101: Test Data). It is evil because it is challenging to handle properly. Test data may be even more dastardly when it is unpredictable, but thankfully there are decent strategies for handling unpredictability.

What is Unpredictable Test Data?

Test data is unpredictable when its values are not explicitly the same every time a test runs. For example, let’s suppose we are writing tests for a financial system that must obtain stock quotes. Mocking a stock quote service with dummy predictable data would not be appropriate for true integration or end-to-end tests. However, stock quotes act like random walks: they change values in real time, often perpetually. The name “unpredictable” could also be “non-deterministic” or “uncertain.”

Below are a few types of test data unpredictability:

  • Values may be missing, mistyped, or outside of expected bounds.
  • Time-sensitive data may change rapidly.
  • Algorithms may yield non-deterministic results (like for machine learning).
  • Data formats may change with software version updates.
  • Data may have inherent randomness.

Strategies for Handling Unpredictability

Any test data is prone to be unpredictable when it comes from sources external to the automation codebase. Test must be robust enough to handle the inherent unpredictability. Below are 5 strategies for safety and recovery. The main goal is test completion – pass or fail, tests should not crash and burn due to bad test data. When in doubt, skip the test and log warnings. When really in doubt, fail it as a last resort.

Automation’s main goal is to complete tests despite unpredictability in test data.

#1: Make it Predictable

Ask, is it absolutely necessary to fetch data from unpredictable sources? Or can they be avoided by using predictable, fake data? Fake data can be provided in a number of ways, like mocks or database copies. It’s a tradeoff between test reliability and test coverage. In a risk-based test strategy, the additional test coverage may not be worthwhile if all input cases can be covered with fake data. Nevertheless, unpredictable data sometimes cannot or should not be avoided.

#2: Write Defensive Assertions

When reading data, make assertions to guarantee correctness. Assertions are an easy way to abort a test immediately if any problems are found. Assertions could make sure that values are not null, contain all required pieces, and fit the expected format.

#3: Handle Healthy Bounds

Tests using unpredictable data should be able to handle acceptable ranges of values instead of specific pinpointed values. This could mean including error margins in calculations or using regular expressions to match strings. Assertions may need to do some extra preliminary processing to handle ranges instead of singular values. Any anomalies should be reported as warnings.

For the stock quote example, the following would be ways to handle healthy bounds:

  • Abort if the quote value is non-numeric or negative.
  • Warn if the value is $0 or greater than $1M.
  • Continue for values between $0 and $1M.

#4: Scrub the Data

Sometimes, data problems can be “scrubbed” away. Formats can be fixed, missing values can be populated, and given values can be adjusted or filtered. Scrubbing data may not always be appropriate, but if possible, it can mean a test will be completed instead of aborted.

#5: Do Retries

Data may need to be fetched again if it isn’t right the first time. Retries are applicable for data that changes frequently or is random. The automation framework should have a mechanism to retry data access after a waiting period. Set retry limits and wait times appropriately – don’t waste too much time. Retries should also be done as close to the point of failure as possible. Retrying the whole test is possible but not as efficient as retrying a single service call.

Final Advice

Unpredictable test data shouldn’t be a show-stopper – it just need special attention. Nevertheless, try to limit test automation’s dependence on external data sources.

Cucumber-JVM for Java

This post is a concise-yet-comprehensive overview of Cucumber-JVM for Java. It is an introduction, a primer, a guide, and a reference. If you are new to BDD, please learn about it before using Cucumber-JVM.

Introduction

cucumber-logo-d727c551ce-seeklogo-com

Cucumber is an open-source software test automation framework for behavior-driven development. It uses a business-readable, domain-specific language called Gherkin for specifying feature behaviors that become tests. The Cucumber project started in 2008 when Aslak Hellesøy released the first version of the Cucumber framework for Ruby.

Cucumber-JVM is the official port for Java. Every Gherkin step is “glued” to a step definition method that executes the step. The English text of a step is glued using annotations and regular expressions. Cucumber-JVM integrates nicely with other testing packages. Anything that can be done with Java can be handled by Cucumber-JVM. Cucumber-JVM is ideal for black-box, above-unit, functional tests.

[Update on 7/29/2018: As of version 3.0.0, Cucumber-JVM no longer supports JVM languages other than Java – namely Groovy, Scala, Clojure, and Gosu. Please read Cucumber-JVM is dropping support of JVM Languages on the official Cucumber blog. The Gherkin parser is also now written in Go instead of in Java.]

Example Projects

Github contains two Cucumber-JVM example projects for this guide:

The projects use Java, Apache Maven, Selenium WebDriver, and AssertJ. The README files include practice exercises as well.

Prerequisite Skills

To be successful with Cucumber-JVM for Java, the following skills are required:

Prerequisite Tools

Test machines must have the Java Development Kit (JDK) installed to build and run Cucumber-JVM tests. They should also have the desired build tool installed (such as Apache Maven). The build tool should automatically install Cucumber-JVM packages through dependency management.

An IDE such as JetBrains IntelliJ IDEA (with the Cucumber for Java plugin) or Eclipse (with the Cucumber JVM Eclipse Plugin) is recommended for Cucumber-JVM test automation development. Software configuration management (SCM) with a tool like Git is also strongly recommended.

Versions

Cucumber-JVM 2.0 was released in August 2017 and should be used for new Cucumber-JVM projects. Releases may be found under Maven Group ID io.cucumber. Older Cucumber-JVM 1.x versions may be found under Maven Group ID info.cukes.

Build Management

Apache Maven is the preferred build management tool for Cucumber-JVM projects. All Cucumber-JVM packages are available from the Maven Central Repository. Maven can automatically run Cucumber-JVM tests as part of the build process. Projects using Cucumber-JVM should follow Maven’s Standard Directory Layout. The examples use Maven. Gradle may also be used, but it requires extra setup.

Every Maven project has a POM file for configuration. The POM should contain appropriate Cucumber-JVM dependencies. There is a separate package for each JVM language, dependency injection framework, and underlying unit test runner. Since Cucumber-JVM is a test framework, its dependencies should use test scope. Check io.cucumber on the Maven site for the latest packages and versions.

Project Structure

Cucumber-JVM test automation has the same layered approach as other BDD frameworks:

BDD Automation Layers.png

The higher layers focus more on specification, while the lower layers focus more on implementation. Gherkin feature files and step definition classes are BDD-specific.

Cucumber-JVM tests may be included in the same project as product code or in a separate project. Either way, projects using Cucumber-JVM should follow Maven’s Standard Directory Layout: test code should be located under src/test.

Cucumber-JVM Example Project

Screenshot of the example project from IntelliJ IDEA’s Project view.

Gherkin Feature Files

Gherkin feature files are text files that contain Gherkin behavior scenarios. They use the “.feature” extension. In a Maven project, they belong under src/test/resources, since they are not Java source files. They should also be organized into a sensible package hierarchy. Refer to other BDD pages for writing good Gherkin.

Gherkin Feature File

A feature file from the example projects, opened in IntelliJ IDEA.

Step Definition Classes

Step definition classes are Java classes containing methods that implement Gherkin steps. Step def classes are like regular Java classes: they have variables, constructors, and methods. Steps are “glued” to methods using regular expressions. Feature file scenarios can use steps from any step definition class in the project. In a Maven project, step defs belong in packages under src/test/java, and their class names should end in “Steps”.

The Basics

Below is a step definition class from the cucumber-jvm-java-example project, which uses the traditional method annotation style for step defs as part of the cucumber-java package. Each method should throw Throwable so that exceptions are raised up to the Cucumber-JVM framework.

package com.automationpanda.example.stepdefs;

import com.automationpanda.example.pages.GooglePage;
import cucumber.api.java.After;
import cucumber.api.java.Before;
import cucumber.api.java.en.Given;
import cucumber.api.java.en.Then;
import cucumber.api.java.en.When;
import org.openqa.selenium.WebDriver;
import org.openqa.selenium.chrome.ChromeDriver;

import static org.assertj.core.api.Assertions.assertThat;

public class GoogleSearchSteps {

  private WebDriver driver;
  private GooglePage googlePage;

  @Before(value = "@web", order = 1)
  public void initWebDriver() throws Throwable {
    driver = new ChromeDriver();
  }

  @Before(value = "@google", order = 10)
  public void initGooglePage() throws Throwable {
    googlePage = new GooglePage(driver);
  }

  @Given("^a web browser is on the Google page$")
  public void aWebBrowserIsOnTheGooglePage() throws Throwable {
    googlePage.navigateToHomePage();
  }

  @When("^the search phrase \"([^\"]*)\" is entered$")
  public void theSearchPhraseIsEntered(String phrase) throws Throwable {
    googlePage.enterSearchPhrase(phrase);
  }

  @Then("^results for \"([^\"]*)\" are shown$")
  public void resultsForAreShown(String phrase) throws Throwable {
    assertThat(googlePage.pageTitleContains(phrase)).isTrue();
  }

  @After(value = "@web")
  public void disposeWebDriver() throws Throwable {
    driver.quit();
  }
}

Alternatively, in Java 8, step definitions may be written using lambda expressions. As shown in the cucumber-jvm-java8-example project, lambda-style step defs are more concise and may be defined dynamically. The cucumber-java8 package is required:

package com.automationpanda.example.stepdefs;

import com.automationpanda.example.pages.GooglePage;
import cucumber.api.Scenario;
import cucumber.api.java8.En;
import org.openqa.selenium.WebDriver;
import org.openqa.selenium.chrome.ChromeDriver;

import static org.assertj.core.api.Assertions.assertThat;

public class GoogleSearchSteps implements En {

  private WebDriver driver;
  private GooglePage googlePage;

  // Warning: Make sure the timeouts for hooks using a web driver are zero

  public GoogleSearchSteps() {
    Before(new String[]{"@web"}, 0, 1, (Scenario scenario) -> {
      driver = new ChromeDriver();
    });
    Before(new String[]{"@google"}, 0, 10, (Scenario scenario) -> {
      googlePage = new GooglePage(driver);
    });
    Given("^a web browser is on the Google page$", () -> {
      googlePage.navigateToHomePage();
    });
    When("^the search phrase \"([^\"]*)\" is entered$", (String phrase) -> {
      googlePage.enterSearchPhrase(phrase);
    });
    Then("^results for \"([^\"]*)\" are shown$", (String phrase) -> {
      assertThat(googlePage.pageTitleContains(phrase)).isTrue();
    });
    After(new String[]{"@web"}, (Scenario scenario) -> {
      driver.quit();
    });
  }
}

Either way, steps from any feature file are glued to step definition methods/lambdas from any class at runtime:

Step Def Glue

Gluing a Gherkin step to its Java definition using regular expressions. IDEs have features to automatically generate definition stubs for steps.

For best practice, class inheritance should also be avoided – step bindings in superclasses will trigger DuplicateStepDefinitionException exceptions at runtime, and any step definition concern handled by inheritance can be handled better with other design patterns. Class constructors should be used primarily for dependency injection, while setup operations should instead be handled in Before hooks.

Hooks

Scenarios sometimes need automation-centric setup and cleanup routines that should not be specified in Gherkin. For example, web tests must first initialize a Selenium WebDriver instance. Step definition classes can have Before and After hooks that run before and after a scenario. They are analogous to setup and teardown methods from other test frameworks like JUnit. Hooks may optionally specify tags for the scenarios to which they apply, as well as an order number. They are similar to Aspect-Oriented Programming. After hooks will run even if a scenario has an exception or abortive assertion – use them for cleanup routines instead of Gherkin steps to guarantee cleanup runs.

The code snippet below shows Before and After hooks from the traditional-style example project. The order given to the Before hooks guarantees the web driver is initialized before the page object is created.

  @Before(value = "@web", order = 1)
  public void initWebDriver() throws Throwable {
    driver = new ChromeDriver();
  }

  @Before(value = "@google", order = 10)
  public void initGooglePage() throws Throwable {
    googlePage = new GooglePage(driver);
  }

  @After(value = "@web")
  public void disposeWebDriver() throws Throwable {
    driver.quit();
  }

Before and After hooks surround scenarios only. Cucumber-JVM does not provide hooks to surround the whole test suite. This protects test case independence but makes global setup and cleanup challenging. The best workaround is to use the singleton pattern with lazy initialization. The solution is documented in Cucumber-JVM Global Hook Workarounds.

Dependency Injection

Cucumber-JVM supports dependency injection (DI) as a way to share objects between step definition classes. For example, steps in different classes may need to share the same web driver instance. Cucumber-JVM supports many DI modules, and each has its own dependency package. As a warning, do not use static variables for sharing objects between step definition classes – static variables can break test independence and parallelization.

PicoContainer is the simplest DI framework and is recommended for most needs. Dependency injection hinges upon step definition class constructors. Without DI, step def constructors must not have parameters. With DI, PicoContainer will automatically construct each object in a step def constructor signature and pass them in when the step def object is constructed. Furthermore, the same object is injected into all step def classes that have its type as a constructor parameter. Objects that require constructor parameters should use a holder or caching class to provide the necessary arguments. Note that dependency-injected objects are created fresh for each scenario.

Below is a trivial example for how to apply dependency injection using PicoContainer to initialize the web driver in the example projects. (A more advanced example would read browser type from a config file and set the web driver accordingly.)

public class WebDriverHolder {
  private WebDriver driver;
  public WebDriver getDriver() {
    return driver;
  }
  public void initWebDriver() {
    driver = new ChromeDriver();
  }
}

public class GoogleSearchSteps {
  private WebDriverHolder holder;
  public GoogleSearchSteps(WebDriverHolder holder) {
    this.holder = holder;
  }
  @Before
  public void initWebDriver() throws Throwable {
    if (holder.getDriver() == null)
      holder.initWebDriver();
  }
}

Automation Support Classes

Automation support classes are extra classes outside of the Cucumber-JVM framework itself that are needed for test automation. They could come from the same test project, a separate but proprietary package, or an open-source package. Regardless of the source, they should fold into build management. They can integrate seamlessly with Cucumber-JVM. Step definitions should be very short because the bulk of automation work should be handled by support classes for maximum code reusability.

Popular open-source Java packages for test automation support are:

Page objects, file readers, and data processors also count as support classes.

Configuration Files

Configuration files are extra files outside of the Cucumber-JVM framework that provide environment-specific data to the tests, such as URLs, usernames, passwords, logging/reporting settings, and database connections. They should be saved in standard formats like CSV, XML, JSON, or Java Properties, and they should be read into memory once at the start of the test suite using global hook workarounds. The automation code should look for files at predetermined locations or using paths passed in as environment variables or properties.

Not all test automation projects need config files, but many do. Never hard-code config data into the automation code. Avoid non-text-based formats like Microsoft Excel so that version control can easily do diffs, and avoid non-standard formats that require custom parsers because they require extra development and maintenance time.

Running Tests

Cucumber-JVM tests may be run in a number of ways.

Using JUnit or TestNG

The cucumber-junit and cucumber-testng packages enable JUnit and TestNG respectively to run Cucumber-JVM tests. They require test runner classes that provide CucumberOptions for how to run the tests. A project may have more than one runner class. The example projects use the JUnit runner like this:

package com.automationpanda.example.runners;

import cucumber.api.CucumberOptions;
import cucumber.api.junit.Cucumber;
import org.junit.runner.RunWith;

@RunWith(Cucumber.class)
@CucumberOptions(
  plugin = {"pretty", "html:target/cucumber", "junit:target/cucumber.xml"},
  features = "src/test/resources/com/automationpanda/example/features",
  glue = {"com.automationpanda.example.stepdefs"})
public class PandaCucumberTest {
}

JUnit and TestNG runners can also be picked up by build management tools. For example, Maven will automatically run any runner classes named *Test.java during the test phase and *IT.java during the verify phase. Be sure to include the clean option to delete old test results. Avoid duplicate test runs by making sure runner classes do not cover the same tests – use tags to avoid duplicate coverage.

Using the Command Line Runner

Cucumber-JVM provides a CLI runner that can run feature files directly from the command line. To use it, invoke:

java cucumber.api.cli.Main

Run with “–help” to see all available options.

Using IDEs

Both JetBrains IntelliJ IDEA (with the Cucumber for Java plugin) and Eclipse (with the Cucumber JVM Eclipse Plugin) are great IDEs for Cucumber-JVM test development. They provide features for linking steps to definitions, generating definition stubs, and running tests with various options.

Cucumber Options

Cucumber options may be specified either in a runner class or from the command line as a Java system property. Set options from the command line using “-Dcucumber.options” – it will work for any java or mvn command. To see all available options, set the options to “–help”, or check the official Cucumber-JVM doc page.

The most useful option is probably the tags option. Selecting tags to run dynamically at runtime, rather than statically in runner classes, is very useful. In Cucumber-JVM 2.0, tag expressions use a basic English Boolean language:

@automated and @web
@web or @service
not @manual
(@web or @service) and (not @wip)

Older version of Cucumber-JVM used a more complicated syntax with tildes and commas.

Parallel Execution

Parallel test execution greatly reduces total start-to-end testing time. However, it requires additional machines to run tests, tools and config to handle parallel runs, and tests to be written to avoid collisions. As of version 4.0.0, Cucumber-JVM supports parallel execution out of the box. Previously, the most common way to do parallel test runs in Cucumber-JVM was to use a Maven plugin like the Cucumber-JVM Parallel Plugin or the Cucable plugin from Trivago.

[Update on 9/24/2018: Mentioned that Cucumber-JVM supports parallel execution starting with version 4.0.0.]

References

BDD 101: Unit, Integration, and End-to-End Tests

There are many types of software tests. BDD practices can be incorporated into all aspects of testing, but BDD frameworks are not meant to handle all test types. Behavior scenarios are inherently functional tests – they verify that the product under test works correctly. While instrumentation for performance metrics could be added, BDD frameworks are not intended for performance testing. This post focuses on how BDD automation works into the Testing Pyramid. Please read BDD 101: Manual Testing for manual test considerations. (Check the Automation Panda BDD page for the full table of contents.)

The Testing Pyramid

The Testing Pyramid is a functional test development approach that divides tests into three layers: unit, integration, and end-to-end.

  • Unit tests are white-box tests that verify individual “units” of code, such as functions, methods, and classes. They should be written in the same language as the product under test, and they should be stored in the same repository. They often run as part of the build to indicate immediate success or failure.
  • Integration tests are black-box tests that verify integration points between system components work correctly. The product under test should be active and deployed to a test environment. Service tests are often integration-level tests.
  • End-to-end tests are black-box tests that test execution paths through a system. They could be seen as multi-step integration tests. Web UI tests are often end-to-end-level tests.

Below is a visual representation of the Testing Pyramid:

The Testing Pyramid

The Testing Pyramid

From bottom to top, the tests increase in complexity: unit tests are the simplest and run very fast, while end-to-end require lots of setup, logic, and execution time. Ideally, there should be more tests at the bottom and fewer tests at the top. Test coverage is easier to implement and isolate at lower levels, so fewer high-investment, more-fragile tests need to be written at the top. Pushing tests down the pyramid can also mean wider coverage with less execution time. Different layers of testing mitigate risk at their optimal returns-on-investment.

Behavior-Driven Unit Testing

BDD test frameworks are not meant for writing unit tests. Unit tests are meant to be low-level, program-y tests for individual functions and methods. Writing Gherkin for unit tests is doable, but it is overkill. It is much better to use established unit test frameworks like JUnit, NUnit, and pytest.

Nevertheless, behavior-driven practices still apply to unit tests. Each unit test should focus on one main thing: a single call, an individual variation, a specific input combo; a behavior. Furthermore, in the software process, feature-level behavior specs draw a clear dividing line between unit and above-unit tests. The developer of a feature is often responsible for its unit tests, while a separate engineer is responsible for integration and end-to-end tests for accountability. Behavior specs carry a gentleman’s agreement that unit tests will be completed separately.

Integration and End-to-End Testing

BDD test frameworks shine at the integration and end-to-end testing levels. Behavior specs expressively and concisely capture test case intent. Steps can be written at either integration or end-to-end levels. Service tests can be written as behavior specs like in Karate. End-to-end tests are essentially multi-step integrations tests. Note how a seemingly basic web interaction is truly a large end-to-end test:

Given a user is logged into the social media site
When the user writes a new post
Then the user's home feed displays the new post
And the all friends' home feeds display the new post

Making a simple social media post involves web UI interaction, backend service calls, and database updates all in real time. That’s a full pathway through the system. The automated step definitions may choose to cover these layers implicitly or explicitly, but they are nevertheless covered.

Lengthy End-to-End Tests

Terms often mean different things to different people. When many people say “end-to-end tests,” what they really mean are lengthy procedure-driven tests: tests that cover multiple behaviors in sequence. That makes BDD purists shudder because it goes against the cardinal rule of BDD: one scenario, one behavior. BDD frameworks can certainly handle lengthy end-to-end tests, but careful considerations should be taken for if and how it should be done.

There are five main ways to handle lengthy end-to-end scenarios in BDD:

  1. Don’t bother. If BDD is done right, then every individual behavior would already be comprehensively covered by scenarios. Each scenario should cover all equivalence classes of inputs and outputs. Thus, lengthy end-to-end scenarios would primarily be duplicate test coverage. Rather than waste the development effort, skip lengthy end-to-end scenario automation as a small test risk, and compensate with manual and exploratory testing.
  2. Combine existing scenarios into new ones. Each When-Then pair represents an individual behavior. Steps from existing scenarios could be smashed together with very little refactoring. This violates good Gherkin rules and could result in very lengthy scenarios, but it would be the most pragmatic way to reuse steps for large end-to-end scenarios. Most BDD frameworks don’t enforce step type order, and if they do, steps could be re-typed to work. (This approach is the most pragmatic but least pure.)
  3. Embed assertions in Given and When steps. This strategy avoids duplicate When-Then pairs and ensures validations are still performed. Each step along the way is validated for correctness with explicit Gherkin text. However, it may require a number of new steps.
  4. Treat the sequence of behaviors as a unique, separate behavior. This is the best way to think about lengthy end-to-end scenarios because it reinforces behavior-driven thinking. A lengthy scenario adds value only if it can be justified as a uniquely separate behavior. The scenario should then be written to highlight this uniqueness. Otherwise, it’s not a scenario worth having. These scenarios will often be very declarative and high-level.
  5. Ditch the BDD framework and write them purely in the automation programming. Gherkin is meant for collaboration about behaviors, while lengthy end-to-end tests are meant exclusively for intense QA work. Biz roles will write behavior specs but will never write end-to-end tests. Forcing behavior specification on lengthy end-to-end scenarios can inhibit their development. A better practice could be coexistence: acceptance tests could be written with Gherkin, while lengthy end-to-end tests could be written in raw programming. Automation for both test sets could still nevertheless share the same automation code base – they could share the same support modules and even step definition methods.

Pick the approach that best meets the team’s needs.

Test Automation Myth-Busting

Test automation is a vital part of software quality assurance, now more than ever. However, it is a discipline that is often poorly understood. I’ve heard all sorts of crazy claims about automation throughout my career. This post debunks a number of commonly held but erroneous beliefs about automation.

Myth #1: Every test should be automated.

“100% automation” seems to be a new buzz-phrase. If automation is so great, why not automate every test? Not every test is worth automating in terms of return-on-investment. Automation requires significant expertise to design, implement, and maintain. There are limits to how many tests a team can reasonably produce and manage. Furthermore, not all tests are equal. Some require more effort to handle, or may not be run as frequently, or cover less important features. Just because a test could be automated does not mean that it should be automated. Using a risk-based test strategy, tests to automate should be prioritized by highest ROI.

Automated testing does not completely replace manual testing, either. Automated testing is defensive: it protects a code line by consistently running scripted tests for core functionality. However, manual testing is offensive: it uses human expertise to explore features off-script, test-to-break, and evaluate wholistic quality. Returns-on-investment for the same tests are often opposites between automated and manual approaches. Automated and manual testing together fulfill vital, complementary roles.

Myth #2: Automation means we can downsize QA.

Executives often see test automation as a way to automate QA out of a job. This is simply not true: Automation makes QA jobs more efficient and all the more necessary. Automation is software and thus requires strong software development skills. It also requires extra tools, processes, and labor to maintain. The benefit is that more tests can be run more quickly. QA jobs won’t vanish due to automation – they simply assume new responsibilities.

Myth #3: Automation will catch all bugs.

By their very nature, automated tests are “scripted” – each test always follows the same pre-programmed steps. This is a very good thing for catching regression bugs, but it inherently cannot handle new, unforeseen situations. That’s why manual, exploratory testing is needed. Automation, being software, may also have its own bugs. Automation is not a silver bullet.

Myth #4: Automation must be written in the same language as the product code.

Automation must be written in the same programming language as the product code for white-box unit tests. However, any programming language may be used for black-box functional tests. Black-box functional tests (like integration and end-to-end tests) test a live product. There’s no direct connection between the automation code and the product code. For example, a web app could have a REST service layer written in Java, a Web UI frontend written in .NET and JavaScript, and test automation written in Python using requests and Selenium WebDriver. It may be helpful to write automation in the same language as the product so that developers can more easily contribute, but it is not required. Choose the best language for test automation to meet the present needs.

Myth #5: All tests should be such-and-such-level tests.

This argument varies by product type and team. For web apps, it could be phrased as, “All tests should be web UI tests, since that’s how the user interacts with the product.” This is nonsense – different layers of testing mitigate risk at their optimal returns-on-investment. The Testing Pyramid applies this principle. Consider a web app with a service layer in terms of automation – service calls have faster response times and are more reliable than web UI interactions. It would likely be wise to test input combinations at the service layer while focusing on UI-specific functionality only at the web layer.

Myth #6: Unit tests aren’t necessary because the QA team does the testing.

The existence of a QA team or of a black-box automated test suite does not negate the need for unit tests. Unit tests are an insurance policy – they make sure the software programming is fundamentally working. In continuous integration, they make sure builds are good. They are essential for good software development. Many times, I caught bugs in my own code through writing unit tests – bugs that nobody else ever saw because I fixed them before committing code. Personally, I would never want to work on a product without strong unit tests.

Myth #7: We can complete a user story this sprint and automate its tests next sprint.

In Agile Scrum, teams face immense pressure to finish user stories within a sprint. Test automation is often the last part of a story to be done. If the automation isn’t completed by the end of the sprint, teams are tempted to mark the story as complete and finish the test automation in the future. This is a terrible mistake and a violation of Agile principles. Test automation should be included in the definition of done. A story isn’t complete without its prescribed tests! Punting tests into the next sprint merely builds technical debt and forces QA into constant catch-up. To mitigate the risk of incomplete stories, teams should size stories to include automation work, shift left to start QA sooner, or reduce the total sprint commitment size. Incomplete test automation often happens when product code is delivered late or a team’s capacity is overestimated.

Myth #8: Automation is just a bunch of “test scripts.”

It’s quite common to hear developers or managers refer to automated tests as “test scripts.” While this term itself is not inherently derogatory, it oversimplifies the complexity of test automation. “Scripts” sound like short, hacky sequences of commands to do system dirty-work. Test automation, however, is a full stack: in addition to the product under test, automation involves design patterns, dependency packages, development processes, version control, builds, deployments, reporting, and failure triage. Referring to test automation as “scripting” leads to chronic planning underestimations. Automation is a discipline, and the investment it requires should be honored.

 

Do you have any other automation myths to debunk? Share them in the comment section below!

BDD 101: Test Data

How should test data be handled in a behavior-driven test framework? This is a common question I hear from teams working on BDD test automation. A better question to ask first is, What is test data? This article will explain different types of test data and provide best practices for handling each. The strategies covered here can be applied to any BDD test framework. (Check the Automation Panda BDD page for the full table of contents.)

Types of Test Data

Personally, I hate the phrase “test data” because its meaning is so ambiguous. For functional test automation, there are three primary types of test data:

  1. Test Case Values. These are the input and expected output values for test cases. For example, when testing calculator addition “1 + 2 = 3”, “1” and “2” would be input values, and “3” would be the expected output value. Input values are often parameterized for reusability, and output values are used in assertions.
  2. Configuration Data. Config data represents the system or environment in which the tests run. Changes in config data should allow the same test procedure to run in different environments without making any other changes to the automation code. For example, a calculator service with an addition endpoint may be available in three different environments: development, test, and production. Three sets of config data would be needed to specify URLs and authentication in each environment (the config data), but 1 + 2 should always equal 3 in any environment (the test case values).
  3. Ready State. Some tests require initial state to be ready within a system. “Ready” state could be user accounts, database tables, app settings, or even cluster data. If testing makes any changes, then the data must be reverted to the ready state.

Each type of test data has different techniques for handling it.

Test Case Values

There are 4 main ways to specify test case values in BDD frameworks, ranging from basic to complex.

In The Specs

The most basic way to specify test case values is directly within the behavior scenarios themselves! The Gherkin language makes it easy – test case values can be written into the plain language of a step, as step parameters, or in Examples tables. Consider the following example:

Scenario Outline: Simple Google searches
  Given a web browser is on the Google page
  When the search phrase "<phrase>" is entered
  Then results for "<phrase>" are shown
  
  Examples: Animals
    | phrase   |
    | panda    |
    | elephant |
    | rhino    |

The test case value used is the search phrase. The When and Then steps both have a parameter for this phrase, which will use three different values provided by the Examples table. It is perfectly suitable to put these test case values directly into the scenario because the values are small and descriptive.

Furthermore, notice how specific result values are not specified for the Then step. Values like “Panda Express” or “Elephant man” are not hard-coded. The step wording presumes that the step definition will have some sort of programmed mechanism for checking that result links relate to the search phrase (likely through regular expression matching).

Key-Value Lookup

Direct specification is great for small sets of simple values, but one size does not fit all needs. Key-value lookups are appropriate when test data is lengthier. For example, I’ve often seen steps like this:

Given the user navigates to "http://www.somewebsite.com/long/path/to/the/profile/page"

URLs, hexadecimal numbers, XML blocks, and comma-separated lists are all the usual suspects. While it is not incorrect to put these values directly into a step parameter, something like this would be more readable:

Given the user navigates to the "profile" page

Or even:

Given the user navigates to their profile page

The automation would store URLs in a lookup table so that these new steps could easily fetch the URL for the profile page by name. These steps are also more declarative than imperative and better resist changes in the underlying environment.

Another way to use key-value lookup is to refer to a set of values by one name. Consider the following scenario for entering an address:

Scenario Outline: Address entry
  Given the profile edit page is displayed
  When the user sets the street address to "<street>"
  And the user sets the second address line to "<second>"  
  And the user sets the city to "<city>"
  And the user sets the state to "<state>"
  And the user sets the zipcode to "<zipcode>"
  And the user sets the country to "<country>"
  And the user clicks the save button
  Then ...

  Examples: Addresses
    | street | second | city | state | zipcode | country |
    ...

An address has a lot of fields. Specifying each in the scenario makes it very imperative and long. Furthermore, if the scenario is an outline, the Examples table can easily extend far to the right, off the page. This, again, is not readable. This scenario would be better written like this:

Scenario Outline: Address entry
  Given the profile edit page is displayed
  When the user enters the "<address-type>" address
  And the user clicks the save button
  Then ...

  Examples: Addresses
    | address-type |
    | basic        |
    | two-line     |
    | foreign      |

Rather than specifying all the values for different addresses, this scenario names the classifications of addresses. The step definition can be written to link the name of the address class to the desired values.

Data Files

Sometimes, test case values should be stored in data files apart from the specs or the automation code. Reasons could be:

  • The data is simply too large to reasonably write into Gherkin or into code.
  • The data files may be generated by another tool or process.
  • The values are different between environments or other circumstances.
  • The values must be selected or switched at runtime (without re-compiling code).
  • The files themselves are used as payloads (ex: REST request bodies or file upload).

Scenario steps can refer to data files using the key-value lookup mechanisms described above. Lightweight, text-based, tabular file formats like CSV, XML, or JSON work the best. They can parsed easily and efficiently, and changes to them can easily be diff’ed. Microsoft Excel files are not recommended because they have extra bloat and cannot be easily diff’ed line-by-line. Custom text file formats are also not recommended because custom parsing is an extra automation asset requiring unnecessary development and maintenance. Personally, I like using JSON because its syntax is concise and its parsing tools seem to be the simplest in most programming languages.

External Sources

An external dependency exists when the data for test case values exists outside of the automation code base. For example, test case values could reside in a database instead of a CSV file, or they could be fetched from a REST service instead of a JSON file. This would be appropriate if the data is too large to manage as a set of files or if the data is constantly changing.

As a word of caution, external sources should be used only if absolutely necessary:

  1. External sources introduce an additional point-of-failure. If that database or service goes down, then the test automation cannot run.
  2. External sources degrade performance. It is slower to get data from a network connection than from a local machine.
  3. Test case values are harder to audit. When they are in the specs, the code, or data files, history is tracked by version control, and any changes are easy to identify in code reviews.
  4. Test case values may be unpredictable. The automation code base does not control the values. Bad values can fail tests.

External sources can be very useful, if not necessary, for performance / stress / load / limits testing, but it is not necessary for the vast majority of functional testing. It may be convenient to mock external sources with either a mocking framework like Mockito or with a dummy service.

Configuration Data

Config data pertain to the test environments, not the test cases. Test automation should never contain hard-coded values for config data like URLs, usernames, or passwords. Rather, test automation should read config data when it launches tests and make references to the required values. This should be done in Before hooks and not in Gherkin steps. In this way, automated tests can run on any configuration, such as different test environments before being released to production.

Config data can be stored in data files or accessed through some other dependency. (Read the previous section for pros and cons of those approaches.) The config to use should be somehow dynamically selectable when tests run. For example, the path to the config file to use could be provided as a command line argument to the test launch command.

Config data can be used to select test values to use at runtime. For example, different environments may need different test value data files. Conversely, scenario tagging can control what parts of config data should be used. For example, a tag could specify a username to use for the scenario, and a Before hook could use that username to fetch the right password from the config data.

For efficiency, only the necessary config data should be accessed or read into memory. In many cases, fetching the config data should also be done once globally, rather than before each test case.

Ready State

All scenarios have a starting point, and often, that starting point involves data. Setup operations must bring the system into the ready state, and cleanup operations must return the system to the ready state. Test data should leave no trace – temporary files should be deleted and records should be reverted. Otherwise, disk space may run out or duplicate records may fail tests. Maintaining the ready state between tests is necessary for true test independence.

During the Test Run

Simple setup and cleanup operations may be done directly within the automation. For example, when testing CRUD operations, records must be created before they can be retrieved, updated, or deleted. Setup would create a record, and cleanup would guarantee the record’s deletion. If the setup is appropriate to mention as part of the behavior, then it should be written as Given steps. This is true of CRUD operations: “Given a record has been created, When it is deleted, …”. If multiple scenarios share this same setup, then those Given steps should be put into a Background section.

However, sometimes setup details are not pertinent to the behavior at hand. For example, perhaps fresh authentication tokens must be generated for those CRUD calls. Those operations should be handled in Before hooks. The automation will take care of it, while the Gherkin steps can focus exclusively on the behavior.

No matter what, After hooks must do cleanup. It is incorrect to write final Then steps to do cleanup. Then steps should verify outcomes, not take more actions. Plus, the final Then steps will not be run if the test has a failure and aborts!

External Preparation

Some data simply takes too long to set up fresh for each test launch. Consider complicated user accounts or machine learning data: these are things that can be created outside of the test automation. The automation can simply presume that they exist as a precondition. These types of data require tool automation to prepare. Tool automation could involve a set of scripts to load a database, make a bunch of service calls, or navigate through a web portal to update settings. Automating this type of setup outside of the test automation enables engineers to more easily replicate it across different environments. Then, tests can run in much less time because the data is already there.

However, this external preparation must be carefully maintained. If any damage is done to the data, then test case independence is lost. For example, deleting a user account without replacing it means that subsequent test runs cannot log in! Along with setup tools, it is important to create maintenance tools to audit the data and make repairs or updates.

Advice for Any Approach

Use the minimal amount of test data necessary to test the functionality of the product under test. More test data requires more time to develop and manage. As a corollary, use the simplest approach that can pragmatically handle the test data. Avoid external dependencies as much as possible.

To minimize test data, remember that BDD is specification by example: scenarios should use descriptive values. Furthermore, variations should be reduced to input equivalence classes. For example, in the first scenario example on this page, it would probably be sufficient to test only one of those three animals, because the other two animals would not exhibit any different searching behavior.

Finally, be cautioned against randomization in test data. Functional tests are meant to be deterministic – they must always pass or fail consistently, or else test results will not be reliable. (Not only could this drive a tester crazy, but it would also break a continuous integration system.) Using equivalence classes is the better way to cover different types of inputs. Use a unique number counting mechanism whenever values must be unique.

For handling unpredictable test data, check out Unpredictable Test Data.

BDD‑‑; Collaboration without Automation

In the previous post, I described the tradeoffs of using a BDD test automation framework without the full BDD process. But, what about the opposite? What if a team wants to adopt BDD practices without a test framework to support it? Again, behavior-driven practices are beneficial apart from automation, but not without shortcomings.

The Power of Process

BDD should be a refinement, not an overhaul, of Agile software development. All of the problems BDD solves are simply aspects of the development process that must be solved anyway. BDD simply provides formal practices for solving them uniformly. Consider how BDD addresses the following problems:

Problem Solution
Biz, dev, and test roles are siloed and do not talk together much. BDD brings these three roles together in Three Amigos meetings.
Acceptance criteria are missing or poorly defined, wasting in-sprint time. Acceptance criteria are formalized as specifications using Gherkin.
Product features are hard to explain. Scenarios describe individual behaviors in plain language.
Team members have open questions or conflicting views about behaviors. Example Mapping efficiently unifies a team’s understanding and identifies areas for further refinement.
Edge cases are overlooked during testing. Well-defined behavior scenarios capture specifications by example early in development.

All of these problems can be solved through better, behavior-driven practices, and none of them pertain to test automation.

Spec-Less Automation

BDD process improvements don’t necessarily need a BDD framework for test automation. Any test framework could still automate scenario steps. The major difference is that there would be no mechanism to translate Gherkin lines into method/function calls: The automation engineer would simply need to program test cases the “good old-fashioned way.” It would not be much different from translating any other procedure-driven test cases into code.

The weakness of this approach is that specifications are not strongly linked to the test automation. The end-to-end development process is less efficient because behavior scenarios must essentially be rewritten into automation code, rather than becoming part of the automation code. There is also a higher risk that automated test cases won’t cover the actual intention of the test steps. Review and maintenance are more difficult because engineers must always cross-examine the automation code with the Gherkin to make sure they align. All of these problems make it harder to shift left with QA work.

The lack of a behavior-driven test framework is also a double-edged sword for Gherkin steps. On one hand, steps do not need to be scrutinized as strongly in review, since automation code does not directly depend upon them. It is not critical to reuse steps word-for-word or to worry about parameterization. However, sloppy steps can lead to miscommunication and will make adopting a BDD test framework in the future very difficult.

Better Than Nothing

Just like for automation without collaboration, using BDD practices without using a BDD test framework does improve the development process. There aren’t really any disadvantages because the process problems must be solved anyway. A “BDD‑‑;” situation (that’s a postfix decrement, to denote that automation did not follow collaboration) isn’t ideal, but at least it’s better than nothing.

‑‑BDD; Automation without Collaboration

Does it make sense to use a BDD test automation framework on a team that does not follow a Behavior-Driven Development process? I’ve faced this questions a few times recently. Although some BDD benefits will be missing, the answer is still yes, BDD test automation frameworks are still useful apart from a full BDD process. This article covers strengths and weaknesses to explain why.

Strengths

BDD test frameworks force tests to be behavior-driven, not procedure-driven. Behavior-driven tests focus on individual behaviors, making them concise and comprehensible. Impertinent factors are removed from test cases. Imperative details are specified only when necessary. Test reports are more descriptive, and test results are more meaningful. Tests written without a behavior-driven framework are more likely to become long, unnecessarily complicated, and fragile.

BDD test frameworks also provide inherent structure with steps. Steps are the basic building blocks of test cases, regardless of the type of test automation framework used. While almost all run-of-the-mill test frameworks (like JUnit, xUnit.net, or pytest) provide structure to write separate, independent test cases (usually as methods or functions), they lack structure to write separate test case steps. Typically, programmers end up writing test case logic directly into the test methods/functions, or they write ad hoc helper methods/functions/classes to get the job done. This approach often lacks consistency (especially when multiple engineers contribute to the automation code), and thus reusability suffers and duplication creeps in. Gherkin steps are like guide rails for test cases.

Gherkin steps provide easy reusability for rapid development. In a mature automation code base, new test cases can be written using a few short lines of pre-existing steps. And pre-existing steps can be trusted to work because they’ve been tested before. Parametrized steps enable even greater reuse.

Gherkin steps are self-documenting because they are written in plain English. This makes tests easier to do many things:

  • to write, because it provides an outline for the test in plain language
  • to review, because others less familiar with the feature can quickly understand concise scenarios
  • to maintain, because problems can be pinpointed
  • to explain, because non-technical people can’t read code

Much like any other test frameworks, BDD frameworks integrate with other testing packages and design patterns. For example, it is common to use a BDD framework with Selenium WebDriver and the Page Object Model to do Web UI testing. Other common packages for needs like logging, assertions, and REST API calls also work well with BDD frameworks.

Finally, BDD test frameworks open the door to shifting left. They can be the starting point for QA-led BDD. Demonstrating the value in behavior-driven automation can open interest in Three Amigos collaboration, which can then lead to more process improvements and better software quality.

Weaknesses

BDD test frameworks require extra development overhead at first. They aren’t as simple to use as unit-like test frameworks. It also takes a lot of practice to write good Gherkin. I’ve talked with engineers (typically developers) who see the feature file layer as unnecessary “plaster” over test cases. Without full team collaboration and cooperation, the justification for BDD diminishes.

Strict behavior independence may also make execution time less efficient. While steps may be reused, common setup operations must be run for each test. CRUD operations illustrate this point well. In a BDD framework, each operation (create, retrieve, update, delete) would be covered by a separate test scenario. However, the operations are interdependent: a test must create a thing before it can delete the thing. Thus, the delete scenario will borrow some logic from the create scenario. A procedure-driven test could more efficiently stack steps into one test case like this: create, retrieve, update, retrieve, delete, retrieve. Assertions would be interleaved with operations. This one test case would cover multiple behaviors, but it would save execution time by avoiding repeated creations for setup and deletions for cleanups. Many times, people have even asked me if there is a way to sequence Gherkin scenarios together to achieve the same effect! (This is not possible, and it would violate test independence.)

If BDD frameworks are used without a BDD process, then BDD could become pigeonholed as a “QA thing,” forever banished to the realm of the far right (the opposite of shift left, not the political spectrum). This could raise barriers to collaboration if not handled properly.

Furthermore, the lack of the full BDD means that many BDD benefits will go missing. Miscommunications could still easily happen because biz and dev would not be involved in defining behavior scenarios. Delivery deadlines could still be missed because testing and automation cannot readily shift left. Out of the 12 major benefits of BDD, the first 4 would be lost.

Conclusion

Overall, I think the advantages of BDD test automation frameworks outweigh the disadvantages for most above-unit functional testing needs, regardless of whether or not a team uses a full BDD process. Ideally, a team would embrace full-BDD, but that’s not always reality. A “‑‑BDD;” situation (that’s a prefix decrement, to note that collaboration was missing before automation) can still be seen as a glass half-full.

Who Should Lead BDD?

Behavior-driven development offers great benefits: better communication, easier test automation, and higher code quality. There are many ways for a team to start doing BDD, and naturally, someone needs to stand up and lead the effort. In my experience, adopting BDD is its own process. An evangelist converts team leaders, training sessions are given, and Gherkinized acceptance criteria start being automated. However, not everyone will embrace the changes, especially those across different role types. And big changes take time. Rome wasn’t built in a day, and neither will be a mature, effective BDD process.

This post covers three possible ways to lead BDD adoption, each from one of the Three Amigos roles. Any role can lead the charge, but each will have its unique struggles. These possibilities are advisory but not necessarily prescriptive. If you want to move your team into BDD, use these three approaches as guidelines for crafting a plan that best meets your needs. And, of course, the advice in Winning Support for BDD pertains to all approaches. Furthermore, as you read these approaches, put yourselves in the shoes of roles other than your own, so you can better understand the struggles each role faces.

Note: The approaches below presume that the underlying software development process is Agile Scrum. Nevertheless, they may be tweaked and applied to other processes, like Waterfall or Kanban.

The Starting Point

The starting point for all three approaches below is a “traditional” Agile sprint – one that is not (yet) behavior-driven. Product owners write user stories, developers implement the solutions, and testers test the deliverables. The diagram below shows the the main flow of sprint work in this type of sprint, and it will serve as the basis for illustrating BDD adoption:

Traditional Sprint

The overall flow of a “traditional,” non-behavior-driven Agile sprint. Ceremonies like planning, review, and retrospective should still happen, but the are left out of this diagram to put emphasis on parts affected by BDD.

QA-Led BDD

Circumstances

The most common approach I’ve seen is QA-led BDD adoption, because testers arguably have the most to gain. It is most applicable when the Three Amigos roles (biz, dev, and test) are well-defined and separate. The impetus for QA to lead BDD adoption could be that developers deliver code too late to adequately test and automate within a sprint, or it could be that the QA team is struggling to scale their test automation development. There may also be resistance to BDD from biz and dev roles.

Steps

The sensible path for QA is to start all the way to the right and progressively shift left. This means that the starting point would be test automation. Start by building a solid automation code base. Pick a well-supported BDD framework like Cucumber, SpecFlow, or behave, and start adding scenarios and step definitions. Select scenarios for core product features rather than the latest sprint stories, so that the code base will be populated with the most basic, useful steps. Once the automation code reaches a “critical mass” for step reusability, QA can then proactively classify new test scenarios as automated or manual. Automated tests become easier and easier to write, giving QA more time to be exploratory with manual testing. Ideally, all manual testing would become exploratory.

Then, it’s time to start shifting left. At this point, all Gherkin steps would be in the automation code only, so set up a tool like Pickles to expose the steps to all team members as living documentation. QA should then schedule Three Amigos meetings with biz and dev to proactively discuss user story expectations. In those meetings, QA should start demonstrating how to write acceptance criteria in Gherkin, which then expedites testing. A big win would be if a QA engineer could write a new scenario using only pre-existing, pre-automated steps and then run it successfully on the spot.

Once biz and dev folks are convinced of BDD’s benefits, encourage them to participate in writing Gherkin. When they get comfortable, encourage product owners to write acceptance criteria in Gherkin when they write user stories, and hold Three Amigos meetings before sprint planning as part of grooming. Convince them that for them to help write Gherkin scenarios is a process efficiency for the whole team.

 

This slideshow requires JavaScript.

Struggles

Shifting left is never easy, especially when team members are hardened into their roles. That’s why QA must write both really good test automation and really good Gherkin scenarios. Success should speak for itself once QA delivers good automation fast. Furthermore, QA must be clear that BDD is not merely a test tool, it’s a process that requires a paradigm shift. Otherwise, BDD could be easily pigeonholed to be a “QA thing.”

Dev-Led BDD

Circumstances

There are a few reasons that could push developers to lead BDD. On some Agile teams, there’s no distinction between dev and QA roles: all team members are software engineers responsible for both developing and testing the software. Or, developers may not be satisfied with the testing effort. Maybe too many bugs are escaping the sprint, or maybe automation isn’t getting done in time. Or, perhaps the product owner is not happy with the deliverables and putting pressure on the team to do better. Whatever the circumstance, developers are more than capable of winning with BDD.

Steps

The best way for developers to start is to set up Three Amigos meetings, to stop the game of telephone between biz and test. In those meetings, start translating acceptance criteria into Gherkin. Then, start helping out with test automation – that may mean anything from offering advice to QA to building the framework from scratch. Then, start pushing left and right to get biz and test on board with BDD.

 

This slideshow requires JavaScript.

Struggles

It may be difficult for developers to work on test automation because they may lack either the expertise or the time to devote to good test automation. Automation is a specialized discipline, and it takes time and diligence to build up expertise to do it right. I’ve seen very skilled developers haughtily build very shabby automation frameworks.

Developers must also be careful to not be too technical, or else biz and test roles may reject BDD for being too complicated or beyond their abilities. Furthermore, some teams may be resistant to developing test automation. For example, automation work may be “starved” for points because it is underestimated or similarly starved for time because it is deemed lower in priority to other work.

Biz-Led BDD

Circumstances

BDD is designed to bring technical and business roles together into healthier collaboration, and biz folks can certainly lead BDD adoption as successfully as more technical folks can. Major reasons for biz to take the lead could be if development is perpetually running behind schedule, if deliverables don’t meet the original requirements, or if software bugs are rampant.

Steps

For biz roles, “shift left” could be better called “pull left.” Start by writing solid user stories and Gherkin acceptance criteria. Focus on good Gherkin that is readable and reusable. Then, introduce BDD as a refinement to the Agile process, highlighting its benefits. Initiate Three Amigos meetings to make sure that you are communicating the right things to dev and test. Once collaboration is going well, suggest BDD automation as a way to expedite dev and test work. If acceptance criteria are all Gherkinized, then developing BDD automation would be a natural extension.

 

This slideshow requires JavaScript.

Struggles

In my experience, biz roles (specifically product owners) tend to be the most hesitant about BDD. They often see writing Gherkin as a burdensome requirement rather than a way to help their team. Or, they may fear that BDD is “too technical” for them. It may also be difficult for them to pitch BDD automation to the team. To be successful, biz roles need to step outside their comfort zone to win supporters from dev and test.

Process paradigm shifts can be hard, especially on teams that are already overwhelmed with work. Some people just don’t like change. Process and automation change can also be a big challenge if QA is outsourced (which is common).

Side-By-Side Comparison

Here’s the TL;DR:

Role Circumstances Steps Struggles
QA
  • Code is delivered too late to test and automate
  • Automation development is not scaling
  1. Build a solid BDD automation framework
  2. Demonstrate automation success
  3. Set up Three Amigos meetings during the sprint
  4. Start writing Gherkin scenarios with biz and dev as part of grooming
  • Showing that BDD is a whole development process, not just a QA thing
  • Getting the team to truly shift left
Dev
  • No separation of dev and QA roles
  • Too many bugs are escaping the sprint
  • Pressure from biz to do better
  1. Initiate better collaboration through Three Amigos and Gherkin
  2. Push right by helping QA with testing and automation
  3. Push left by helping biz write better acceptance criteria
  • Humbly learning good automation practices
  • Dedicating time for automation and more meetings
Biz
  • Missed deadlines
  • Deliverables not matching expectations
  • Too many bugs
  1. Write acceptance criteria in good Gherkin
  2. Set up Three Amigos meetings to review Gherkin
  3. Pitch BDD automation
  • Learning semi-technical things
  • Pushing all the way to automation

Conclusion

These are just three general approaches intended to show how BDD is for everyone. If you have other approaches, please describe them in the comment section below! Whatever the approach, make sure to demonstrate that BDD helps everyone, or else people may feel forced into corners and reject BDD for bad reasons. And remember, software quality is not just QA’s responsibility; it is everyone’s responsibility.