Clear requirements, confident testing: a practical guide for QA success

There’s one thing that can make or break your IT project: good requirements. Think of them as the foundation of a house; get them wrong and it collapses. Requirements define what software should do, and they guide every step of the project. Without them, teams are flying blind leading to missed deadlines, budget overruns  and software that fails to meet user needs. Investing time and effort into solid requirements is crucial.

The link between requirements and software testing

When it comes to software testing, good requirements are your best friend. They provide you with a clear roadmap. Poorly written requirements leave testers guessing, increasing the risk of critical bugs slipping through. Our top 3 tips regarding good requirements for QA engineers are:

• Be specific: Keep each requirement focused. Avoid cramming multiple features or fixes into a single ticket. Instead, create separate tickets for distinct functionalities. This ensures clarity, prevents miscommunication and makes it easier to track progress.
• Be clear and unambiguous: Requirements should leave no room for interpretation. Use precise language and avoid vague terms like “fast,” “user-friendly” or “efficient” without quantifiable definitions. If a requirement can be understood in multiple ways, it’s likely to cause issues down the line.
• Be complete: Provide all necessary details. Testers and developers shouldn’t have to hunt for missing information. Ensure that every requirement includes relevant acceptance criteria, edge cases and dependencies. A well-documented requirement saves time and reduces the risk of incorrect implementations.

The use of AI when writing requirements

Let’s shift gears and look at where requirements are headed. AI is changing how we define, write and validate them. What used to be a slow, manual process is now supported by tools that flag gaps and remove ambiguity. Some even generate full requirement sets from user stories. AI isn’t just helpful, it’s a powerful boost for QA and business analysts alike.

Some insights

Over the past year, we’ve experimented extensively with writing requirements using AI. Here’s what we’ve learned:

• Give AI context: It performs best when it understands the bigger picture. Providing background information, business goals and relevant constraints helps generate more accurate and relevant requirements.
• Use examples to clarify: Abstract results are common. By including concrete examples, such as expected inputs, outputs or user interactions, you can make them more practical and testable.
• Refine the results: It helps to iterate, challenge vague phrases and ask for refinements to improve clarity.
• Set a format and tone: Consistency is key when writing requirements. Defining a structured format and a clear tone ensures AI-generated content aligns with your team’s expectations and remains readable for all stakeholders.
• Define a scope: AI can generate a flood of information, but without a clear scope it may introduce unnecessary complexity. Clearly outline what needs to be covered to keep the requirements relevant and actionable.

An example:

Requirements and test automation

Good requirements are the foundation of effective test automation. Without structure, automation becomes fragile and hard to maintain. Clear requirements guide what to automate and how to approach it. Teams can also use requirements to choose between commercial tools and custom frameworks.

When considering test automation, teams often face a choice between commercial automation tooling and custom-built automation frameworks:

• Test automation tooling (e.g., Katalon, Leapwork, Tricentis Tosca) often provides low-code or codeless automation solutions with built-in integrations. These tools are great for rapid adoption but may come with vendor lock-in and limited customization options.
• Test automation frameworks (e.g., Selenium, Playwright, Cypress, Appium) provide flexibility and scalability but require strong technical expertise to set up and maintain. This flexibility allows them to tailor automation to their specific needs but demands well-structured requirements to avoid excessive rework.

Defining a test automation approach starts with understanding what needs to be automated, how frequently tests will run and what level of maintainability is required. Good requirements help answer these questions.

For teams just getting started with test automation, strong requirements provide a structured entry point. They help identify critical test scenarios, ensure meaningful test coverage and prevent automating the wrong things. By leveraging solid requirements, teams can avoid common pitfalls and build automation that truly adds value.

The expertise and tools to help you nail your requirements

At Brightest, we know how critical good requirements are to the success of any IT project. As specialists in software quality, we have the expertise and tools to help you nail your requirements from the get-go. Don’t gamble with your project’s success, reach out to us for advice and let us help you ensure your software is top-notch.