Cucumber Testing: A Key to Generative AI in Test Automation

In the world of modern software development, automating testing is a MUST! As teams strive for frequent and reliable releases, Behavior-Driven Development (BDD) frameworks like Cucumber have emerged as solutions to bridge communication gaps between technical and non-technical stakeholders. How can a BDD framework such as Cucumber pave the way for Generative AI in test automation? In this blog, we’ll explore why Cucumber testing remains integral to behavior-driven workflows, and how the Cucumber testing framework allows Generative AI to flourish.

If you’ve ever asked yourself, “How can Generative AI transform my test automation strategy?” or “Is there a synergy between natural language test scenarios and AI models?”, read on. 

What is Cucumber Testing? 

Cucumber testing is a methodology built on top of Behavior-Driven Development (BDD). Originally written in Ruby, Cucumber leverages a domain-specific language called Gherkin, which allows tests to be written in plain English. The simplest definition of cucumber testing is:  

Cucumber is a BDD-focused test automation tool that encourages cross-team collaboration by defining test scenarios in everyday language.

BDD focuses on creating a shared understanding of how software should behave. BDD evolves from Test-Driven Development (TDD) by shifting the focus to “behavior” rather than individual test cases alone.

Key BDD Concepts:

• Given-When-Then Syntax: Offers a clear structure for specifying preconditions (Given), actions (When), and outcomes (Then).
• Business Readability: Ensures that all stakeholders (QA, developers, product owners, business analysts) can contribute to and understand test scenarios.
• Executable Specifications: Transforms human-readable requirements into automated test scripts, bridging communication gaps.

The State of Test Automation and the Rise of Generative AI

Test automation has evolved from linear scripting to more behavior-driven structures. Along with this generative AI has been exploding, opening the door for repetitive tasks to become automation work flows. When we talk about Generative AI in test automation, we’re referring to AI-driven capabilities such as:

• Auto-generating test scenarios from user stories or requirements.
• Adapting tests on the fly based on application changes.
• Offering intelligent test coverage suggestions or risk-based testing.

So, how does cucumber fit in? In short cucumber’s natural language approach to test definition helps large language models understand and generate text creating a more robust and AI-friendly testing workflow. A platform like ChatGPT can take user stories written in plain English and transform them into Gherkin steps to maintain tests. 

Key Components of the Cucumber Testing Framework

To truly grasp how Cucumber can power generative AI applications, it’s vital to understand its building blocks:

Feature Files – Written in Gherkin, each feature file ends with the .feature extension and describes an application feature. Within each file, you can have multiple scenarios that represent different behaviors or requirements.

Scenarios – Each scenario outlines a test case using Given-When-Then steps. 

Step Definitions – Each step definition is in the programming language of your choice and map the plain English statements to executable code.

Test Runner – A test runner like JUnit or TestNG orchestrates the execution of feature files, linking them to their step definitions.

Hooks and Tags – Cucumber provides hooks (@Before, @After, etc.) for setup and teardown activities, and tags (@Smoke, @Regression, etc.) to categorize or filter scenarios.

A Simple Cucumber Testing Example

Let’s consider a basic login scenario—something many applications require. Here’s a cucumber testing example that clarifies the Gherkin syntax:

Gherkin Syntax:

Feature: Login Functionality

  In order to ensure users can access their accounts securely

  As a registered user

  I want to successfully log into the application

  Scenario: User logs in with valid credentials

    Given the user is on the Home page

    When the user navigates to the Login page

    And the user enters "Admin" and "admin123" as credentials

    Then the successful login message is displayed

Corresponding Step Definitions in Java

@Given("the user is on the Home page")

public void theUserIsOnTheHomePage() {

    // Launch browser, navigate to Home page

}

@When("the user navigates to the Login page")

public void theUserNavigatesToTheLoginPage() {

    // Code to click login link

}

@When("the user enters {string} and {string} as credentials")

public void theUserEntersCredentials(String username, String password) {

    // Code to input username and password

}

@Then("the successful login message is displayed")

public void theSuccessfulLoginMessageIsDisplayed() {

    // Code to verify success message

}

Notice the clarity in the feature file, mirrored by the underlying code. This example is a microcosm of how Cucumber fosters a BDD mindset. 

What Challenges Exist within the Cucumber Testing Framework?

Even the most popular tools come with challenges. While Cucumber testing is powerful, here are some potential pitfalls:

1. Maintenance Overhead – As your application grows, you risk duplicating steps across multiple feature files. A single variation in wording creates a whole new step definition unless you proactively refactor.
2. Slow Execution – End-to-end tests that rely on a real browser, database, or external APIs can slow down your CI pipeline—especially if you don’t manage test scope carefully.
3. Limited Collaboration if Used Incorrectly – If only the QA team writes and maintains Cucumber tests, you lose the collaboration it’s meant to foster. Developers and product owners must be involved.
4. Initial Learning Curve – While Gherkin is straightforward, teams need time to structure feature files effectively and avoid anti-patterns (like overcomplicating steps).

Nonetheless, as we will discuss next, the synergy with Generative AI can combat some of these challenges and reduce the overhead for large test suites while speeding up test creation.

How Cucumber Testing Sets the Stage for Generative AI

What is the benefit of linking Cucumber with Generative AI? Well, generative AI excels at understanding and generating human language and is trained using massive datasheets. Since  Cucumber’s Gherkin uses natural language, it is the perfect platform to incorporate Generative AI. 

Cucumber’s Gherkin is:

• Structured yet easy to analyze.
• Representative of functional requirements in plain text.
• Full of domain context about your application’s features.

These properties match perfectly with Generative AI capabilities:

• Analyzing text for intent
• Generating new content
• Suggesting improvements to existing text

The synergy between Gherkin and AI language models could automate or semi-automate many phases of the testing lifecycle.

Automatic Test Creation:

Your team can move away from manually writing features in Gherkin by utilizing AI. By feeding user stories, wireframes or customer feedback into AI, AI can auto-generate the Gherkin scenarios. In this synthesis, BDD is used as the output instead of the starting point. This approach is opposite of the typical process and turns Gherkin into a structured format AI can maintain. 

Test Maintenance:

AI can detect changes in UI text or functionality and propose modifications to the relevant steps. This saves time and reduces the risk of human error when tracking minor UI labels or workflow changes. By suggesting updates to the Gherkin scenarios, AI helps keep your test suite aligned with the latest application state. Testers remain in control, reviewing and approving these changes to ensure that quality standards are met. 

Smarter Coverage:

AI analyzes usage patterns and/or analytics data to proposes scenarios that go beyond typical manual testing. These suggestions often include high-risk paths from real users or corner cases missed by traditional tests. By reflecting real-world behavior, AI’s recommendations help teams create more targeted and effective tests. This ensures that coverage aligns closely with how users actually interact with your product. AI leverages data-driven insight to broaden the testing scope.

When you step back, you realize that BDD frameworks like Cucumber serve as structured repositories of “human + domain” knowledge. Generative AI can exploit that knowledge to scale test coverage especially in continuous delivery pipelines.

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Data-Driven Testing Meets Generative AI

The Cucumber testing tool excels at data-driven testing. Rather than rewriting entire scenarios, you can rely on Scenario Outlines or Data Tables to feed multiple sets of inputs into a single scenario. This approach can further benefit from Generative AI.

Scenario Outlines:

Scenario Outline: User Login

  Given the user is on the login page

  When the user enters <username> and <password>

  Then the user sees <outcome>

  Examples:

    | username   | password   | outcome              |

    | validUser  | validPass  | dashboard page       |

    | invalidUser| invalidPass| error message        |

An AI system can “invent” new rows of test data, especially boundary cases, invalid formats, or less frequently tested combinations.

Data Tables:

Scenario: Successful Login with multiple valid credentials

  Given the user is on the login page

  When the user enters the following credentials:

    | username            | password   |

    | user1@example.com   | password1  |

    | user2@example.com   | password2  |

  Then the user should be redirected to the dashboard for each set of credentials

An AI-based module could generate additional table rows or discover patterns not originally accounted for by the manual author of the feature.

Best Practices to Leverage Cucumber for AI-Driven Test Automation

While the potential is exciting, be careful to not put the cart before the horse. Combining Cucumber and Generative AI requires a thoughtful approach:

1. Keep Gherkin Clean and Consistent – The more consistent your step definitions and feature files, the easier it is for AI to parse and propose enhancements. Adhere to naming conventions (e.g., the user is on the <page> page).
2. Tag Your Tests – Use meaningful tags (@Smoke, @Regression, @Critical) so the AI can differentiate critical vs. non-critical tests. This helps in prioritizing test suggestions or expansions.
3. Adopt a Version Control Strategy – If AI is making changes to your .feature files or step definitions, treat these changes like any other code commit. Use pull requests and code reviews to maintain quality.
4. Human-in-the-Loop – Human oversight remains critical to the process. Validate that any newly created or updated scenarios accurately reflect business logic.
5. Leverage Parallel Execution – With frameworks like JUnit 5, you can run Cucumber tests in parallel. This is especially beneficial if AI is generating a large number of test cases—your test pipeline can scale without significantly increasing execution time.
6. Measure Effectiveness – Implement metrics: how many bugs are caught by AI-suggested test scenarios, how often are AI suggestions accepted vs. rejected, etc. This helps refine the AI model and your approach.

Real-World Use Cases: From Chatbots to Automated Test Writing

The synergy between Cucumber and Generative AI is already a possibility. Here are a few practical examples:

1. Chatbots Assisting in Test Creation – Imagine a chatbot integrated into your IDE or test management platform. You type: “Generate login scenarios for an e-commerce platform with password reset functionality.” The AI then crafts a complete .feature file, from successful login scenarios to negative test cases.
2. AI-Suggested Step Definitions – As you write new Gherkin steps, an AI model might recommend existing step definitions or propose new ones – cutting down duplication.
3. Automated Regression Suite Updates – When your application’s UI changes (say, a new label or a different button text), the AI can highlight which steps might be affected and auto-adjust them, pending human approval.
4. Continuous Exploratory Testing – Beyond predefined scripts, a generative model could propose new user pathways based on application usage analytics.

By harnessing Generative AI, your test suites become living. The ever evolving set of scenarios reflect real-world usage patterns, potential vulnerabilities, and more.

Will Test Cases Become Obsolete? Rethinking the Role of Testers and the V-Model

As AI grows a provocative question emerges: Will test cases themselves become obsolete, potentially collapsing the traditional V-model of software development? If large language models can continuously generate and refine scenarios based on evolving requirements, one could argue that we no longer need to think in terms of fixing “test cases.” Instead, requirements and behaviors become an ever-updating knowledge source, automatically transformed into executable specifications.

From this perspective, testers’ responsibilities could shift upstream. Rather than writing and maintaining static test cases, testers might play a more strategic role in refining requirements and clarifying the system’s intended behavior. Consider the following implications:

1. Continuous Requirement Refinement – The primary value of testers could be to validate the correctness of the AI generated Gherkin Scenarios by comparing them against business goals and user expectations. In this future, testers become collaborators in defining clear, realistic and testable requirements for AI-driven tools. 
2. Dynamic, Living Documentation – Traditional “test cases” might blend into a living document of requirements and behaviors. AI-driven updates could keep documentation in sync with system changes, blurring the lines between specification and validation, challenging the V-model, and allowing tests to loop in real time. 
3. Risk-Based Testing vs. Exhaustive Test Suites – AI can suggest or generate a nearly infinite array of negative tests, boundary tests, and edge cases. However, humans are required to determine which of those represent real business risk and deserve priority. Testers will shift their focus from writing granular test steps to prioritizing tests and managing a risk-based approach.

In short, a tester’s job will remain essential, but their specific tasks will shift. AI won’t eliminate the need for defined tests, but it may make them more adaptable. Testers will become requirement stewards, ensuring that feedback between stakeholders and AI testing stays accurate and valuable.

Conclusion: The Future of Test Automation with Cucumber and Generative AI

The Cucumber testing framework has already proven its worth by improving collaboration and bridging communication gaps in software teams worldwide. As the testing landscape continues to evolve, Generative AI stands to further accelerate test creation, maintenance, and coverage.

By writing tests in plain English (or any of Gherkin’s supported languages), Cucumber provides a structured knowledge base that AI can learn from. In turn, AI can auto-generate or auto-maintain these test scenarios, identify coverage gaps, and propose new “Given-When-Then” flows that might otherwise be missed by humans.

The synergy is clear:

1. Cucumber fosters clarity and collaboration.
2. Generative AI thrives on large, well-structured textual data.

When combined, they form a powerful methodology for building, refining, and scaling automated test suites in ways that were hard to imagine just a few years ago. From common UI validations to complex user journeys, the potential for AI-augmented BDD is limited only by our imagination.

Ready to see how Cucumber can transform your mobile and web testing efforts? Explore Kobiton’s device testing platform and discover how real-device testing, combined with BDD best practices, can give you a head start in the AI-driven era of quality assurance.