Accelerated QA: Gherkin as the Key to Clear Testing

How First Line Software Accelerates QA with Gherkin 

In a Zoom meeting where  FLS testers, engineers, and managers are gathered, the topic of discussion wasn’t the latest AI model or a billion-dollar acquisition. It was syntax. Specifically, the understated power of Gherkin—a plain-text language quietly becoming foundational to quality assurance for modern software teams.

As software development cycles accelerate and interdisciplinary collaboration becomes the norm, clarity is critical. Enter Behavior-Driven Development (BDD), and at its heart, Gherkin—a language designed to align developers, testers, and business stakeholders around a single, readable specification. Behavioral keywords bridge the gap between technical and non-technical people. 

Let’s look at a few examples, as well as how our team at First Line Software is already leveraging Gherkin. 

Why Accelerated QA Matters

Gherkin scenarios often read like simple stories. They describe how software should behave from a user’s perspective, clarifying intent without the need for technical translation. Take a look at the following scenario, which verifies that specific products are correctly listed in the EStore product listing page with their names, IDs, prices, and placeholder images, and checks the ability to switch between grid and list view formats:

While it may resemble a set of user instructions, this syntax doubles as test automation logic. Each line corresponds to backend code written in C#. The bridge from specification to implementation is direct:

The result? This seamless mapping turns documentation into working code. Requirements are no longer static Word documents; they’re living artifacts embedded in the development process.

Driving Accelerated QA with Unified Test Scenarios

Using Gherkin helps teams avoid duplicate effort. If a test fails, it signals that something has changed in the system. That failure becomes an alert not just to engineers, but also to product managers, QA analysts, technical writers, and other stakeholders who rely on up-to-date documentation to make informed decisions, update user guides, or ensure product quality.

Moreover, Gherkin scenarios can serve as blueprints for automation engineers. Testers can describe what should happen, and Test Automation Engineers simply link those descriptions to functions, saving time, avoiding ambiguity, and ensuring consistency.

An additional advantage is that Gherkin scenarios, being tightly coupled with automated tests, always reflect the current state of the system. As the implementation evolves, outdated scenarios naturally cause test failures, prompting timely updates. This keeps the documentation continuously up to date and reliable for both technical and non-technical stakeholders.

Real-World Challenges and Solutions

Transitioning from traditional test case management systems like TestRail to Gherkin-based frameworks presents a significant challenge: the manual rewriting of extensive test libraries. While First Line Software manages thousands of such cases, the task is both time-consuming and prone to redundancy.

To address this, we developed an internal tool for semantic search, which is designed to streamline the conversion process. Unlike conventional keyword-based searches, this tool leverages semantic understanding to interpret the intent behind queries. For instance, when a tester inputs a phrase like “authorize user,” the tool intelligently retrieves relevant existing steps, such as Given the user is logged in, even if there’s no direct textual match.

This capability is powered by integrating sentence-transformer-based embeddings with a vector similarity search engine (FAISS). The transformer model encodes Gherkin test steps into high-dimensional vectors that capture the semantic meaning of each step. FAISS enables efficient approximate nearest neighbor search in this vector space. This combination allows the tool to:

• Interpret Semantic Meaning: By analyzing the meaning of tester queries rather than relying on exact text match, the system can identify and recommend semantically similar steps even if they are worded differently.
• Promote Reuse of Existing Steps: Instead of creating new Gherkin steps for similar actions, testers are guided to reuse already implemented steps from the automation codebase, improving consistency and reducing maintenance effort.
• Boost Authoring Efficiency: The tool accelerates the process of writing new Gherkin-based test cases by minimizing the manual effort required to search through thousands of existing steps.
• Bridge Manual and Automation Workflows: Manual testers can author test cases directly in Gherkin notation, relying on the tool to ensure alignment with available automated steps, facilitating a smoother handoff to automation engineers.

This innovative approach not only simplifies the transition to Gherkin but also fosters a more collaborative environment. Manual testers, regardless of their technical background, can contribute effectively to the automation process, ensuring that the entire team speaks a unified language when it comes to software behavior specifications.

Edge Cases and Limitations

Despite its benefits, the semantic search approach is not without tradeoffs:

• False Positives: Occasionally, steps with different context but similar wording are retrieved (e.g., “click confirm”vs “confirm email address”).
• Ambiguous Queries: Queries like “log” could map to logging in or log collection unless further disambiguated.
• Embedding Drift: If embeddings are regenerated with a different model or version, the semantic distance scale may shift slightly, requiring recalibration of thresholds or cut-off values.

To mitigate these, we plan to introduce context filtering and interactive relevance feedback in future iterations.

Building a Shared Language

First Line Software’s manual testing team has already converted 10 test cases into Gherkin, with plans to scale further and track improvements in automation speed. Early signs are promising. 

The ambition is not just efficiency—it’s alignment. Gherkin transforms quality assurance from a specialized silo into a shared practice. It’s not about using a tool, it’s about creating a common language that everyone on the team can speak.

As software complexity grows, so does the need for clarity. And in an industry obsessed with speed and scale, Gherkin’s quiet promise of shared understanding may be its most radical feature yet.

We’ve already begun rolling out Gherkin-first practices across several projects, starting with a focused pilot. Within weeks, our manual QA team successfully converted their first batch of test cases using semantic support, and automation engineers reported faster integration times.

Looking ahead, we expect to expand this approach across more delivery teams, capture data on time saved, and measure improvements in test stability and cross-role collaboration.

Future Enhancements

This prototype lays the groundwork for broader innovations in test automation. Future directions may include:

• Active Learning Loop: Let the system learn from tester feedback (“this match was useful”) to refine similarity results.
• LLM-assisted Normalization: Use LLMs (locally or via API) to rewrite non-Gherkin test case steps into canonical Gherkin phrasing before semantic search.
• IDE Plugin Integration: Embed the search UI directly into Rider/VS Code with autocomplete overlays.

To learn more about our Gherkin-first pilot projects, reach out here.