Automating Documentation with AI: A QA Engineer's Guide

Writing documentation, such as test cases and checklists, can be a tedious and time-consuming task for QA engineers. While testing APIs, analyzing UI behavior, and finding bugs can be engaging, the repetitive nature of documentation often leads to fatigue and a desire for a more efficient solution. This article explores how AI can alleviate this pain.

The first step involves providing the AI with a clear task definition. This can be done by uploading a screenshot of the screen in question or describing the functionality for which a checklist is needed. Providing additional context, such as where the screen is used, the available controls, and the expected behavior, can further enhance the AI's understanding. The AI will then generate an initial draft of the checklist, typically covering basic checks like button display, click response, and screen transitions. While this initial output may not be perfect, it serves as a valuable starting point.

This is where the iterative process begins. The AI-generated checklist is reviewed, and feedback is provided to address any shortcomings. Common issues include forgetting 'Back' and 'Close' buttons, failing to distinguish between headings and interactive elements, ignoring icon behavior, and overlooking non-standard patterns. Specific feedback is provided to guide the AI, often detailing the desired outcome. Through several iterations, the AI adapts and produces a more refined output that is ready for practical use.

Once the checklist's content is satisfactory, the AI is instructed to format it according to specific requirements. This may involve defining levels of decomposition (sections and steps), numbering each item, applying a specific writing style, and structuring the data with fields like ID, Screen, Expected Result, Priority, and Behavior. To ensure the AI understands the desired format, a template or PDF with the requirements can be provided as a reference. Examples of correct and incorrect formatting can also be given to further clarify expectations.

The next step involves adding metadata and attributes that the AI may not automatically include. This could include Priority (High/Medium/Low), Behavior Type (Positive/Negative), Component (for module linking), and links to requirements. If the AI omits any of these attributes, explicit instructions or examples are provided to guide its behavior. Visual aids, such as screenshots demonstrating the desired output, can also be effective.

Once the checklist meets the required standards, it is exported to a format compatible with test management systems. CSV is a particularly convenient format, as the AI can generate a table with each row representing a separate check and all fields in the correct order for import into tools like TestRail, Qase, or Allure TestOps. Other formats, such as Markdown or JSON, can also be used depending on the destination of the documentation.

The initial setup and training of the AI may take around 40 minutes. However, once the AI is trained and the dialogue is saved, subsequent tasks can be completed 3-5 times faster. It's crucial to maintain the session where the iterations were performed, as it serves as a working environment where the AI remembers the established style, structure, and requirements.

It's important to recognize that AI rarely delivers perfect results on the first attempt. Each step requires a cycle of reading, correcting, refining, and comparing against expectations. Blindly trusting AI is not advisable, as it is a helpful but imperfect tool. A solid understanding of what constitutes a good result is essential for catching errors. Therefore, AI is best suited for those who already possess the skills to write documentation manually. Templates should be adapted to the specific task, and new projects may require adjustments. Legal risks should also be considered, and sensitive information should be anonymized when working under NDAs.

If writing documentation feels like a challenging quest, consider giving AI a try. While some initial setup is required, it can transform into a genuinely useful tool. For those already using AI in testing, sharing tips and tricks can accelerate the industry's transition from manual processes to a more automated and efficient approach.