Most software testing doesn’t happen in flashy tools or automation frameworks. It takes place in the background: in changelogs, bug reports, requirement traceability, and regulatory documentation. These details may not draw attention, but they are essential for releasing safe, reliable, and well-tested software. It’s unglamorous, but vital.
Generative AI (GenAI) is becoming genuinely useful here, not by writing test cases, but by helping QA teams manage the growing complexity of release documentation.
During a typical sprint, QA engineers often need to read through dozens or even hundreds of documents. These include test logs, bug summaries, configuration files, feature diffs, and stakeholder feedback. Sorting through all this information by hand is slow and difficult to scale. GenAI tools help by summarizing documents, flagging important changes, identifying gaps, and linking related information together.
For example, imagine uploading your entire release folder and asking questions like:
- What changed in the encryption module?
- Which new features haven’t been tested yet?
- Are there any gaps between requirements and test coverage?
With the right setup, GenAI can answer these questions accurately and point to specific source files or entries. It does not need to “understand” your system like a human does. Instead, it reads across structured and unstructured documentation and makes connections based on patterns and content.
This approach works particularly well in regulated environments, where traceability is required. GenAI can help build or verify requirement-to-test-case links, highlight missing verifications, and even suggest where release notes may be incomplete.
This is high-impact, low-risk GenAI. It doesn’t act on code. It doesn’t touch production. But it gives you better visibility and confidence about what you're shipping. For teams looking to adopt GenAI in a meaningful way, start here. Release documentation is messy, critical, and ripe for automation. And the benefits compound with every release cycle.
Challenges GenAI Helps Software QA Teams Solve
- Large volumes of test plans, logs, and reports across releases
- Incomplete or inconsistent changelogs
- Manually built summaries that miss hidden risks
- Traceability matrices that are outdated or incomplete
Where GenAI Performs Best in QA Documentation
- Extracting key changes between software versions
- Summarizing bug reports and test outcomes
- Identifying undocumented features or changes
- Spotting unlinked requirements or test cases in regulated projects
How to Build Retrieval-Augmented Generation QA (RAG-QA)
- Store your release data in a vector database to enable fast, semantic search
- Use internal QA chatbots trained on your documentation to answer release-specific questions
- Auto-generate draft test case outlines based on what changed from the last version
Real-World Impact of Using GenAI in Release QA
- Save hours of manual QA documentation work per release
- Strengthen traceability for audits and compliance reviews
- Reduce risk by finding untested or undocumented changes early
Practical Considerations When Using GenAI in QA Workflows
Generative AI brings clear value to software quality processes, particularly in accelerating documentation review, traceability analysis, and release validation. But to unlock its full potential in real-world QA workflows, it must be combined thoughtfully with purpose-built tools and processes.
1. GenAI may misinterpret complex QA data
AI models can generate fluent but inaccurate summaries when working with release notes, test logs, or traceability matrices. For regulated or safety-critical domains, combining GenAI with specialized QA tools, such as test management systems, static analysis, or GUI automated testing, ensures factual grounding and context-aware output.
2. Reproducibility and auditability require structure
Because GenAI outputs can vary across prompts and sessions, it's important to enforce consistency. Pairing AI with structured toolchains, versioned artifacts, prompt templates, and human-in-the-loop reviews, helps maintain reliability and traceability across releases.
3. Domain-specific relevance matters
Generic models may lack the technical depth needed for embedded systems, automotive software, or IEC/ISO-regulated environments. Augmenting GenAI with QA tools that already manage system context (like requirement management or defect tracking platforms) ensures insights are both accurate and actionable.
Bottom line: GenAI is most powerful when integrated—not isolated
Used in combination with existing QA platforms and toolchains, GenAI becomes a smart assistant that enhances, not replaces human judgment. It brings speed and clarity to complex documentation and review tasks while leaving control and accountability where it belongs: with experienced QA teams.
Integrating GenAI with Software QA Tools
The impact of GenAI becomes even more powerful when paired with specialized QA tools already in use. For example, let's take Squish for GUI testing.
In a recent approach shared, teams used GenAI to generate and refine GUI test scripts by combining recorded UI maps, product documentation, and test logs. The assistant could run tests, analyze results, and update scripts automatically, freeing QA engineers from repetitive coding tasks and reducing the time spent maintaining tests after UI changes.
This also improves the quality of release documentation. The combination of tools used by the AI assistants, rather than centralized services, allows for greater flexibility. As Squish tests evolve, combined with other QA tools, GenAI can track what has been tested, summarize results, and highlight gaps, making it easier to prepare test evidence, verify coverage against requirements, and support release reviews. Instead of searching through raw test logs, teams get clear summaries linked to documented changes and test cases.
For teams reviewing releases under pressure, this pairing of GenAI and QA tools turns test execution data into usable, review-ready insight faster and with fewer manual steps.
Conclusion
Software QA is about making sense of change. Every release generates documents that tell the story of that change, and that story is critical. Generative AI helps QA teams read, summarize, and connect that story faster and more clearly.
If your testing team is under pressure to move faster without losing traceability or control, this is one of the best places to apply GenAI. It handles the mess of documents so that you can focus on the critical decisions and projects.
What's next
Watch the Panel Discussion: Maximize the Potential of AI in Quality Assurance featuring the insights by the AI practitioners:
Peter Schneider, Principal, Product Management at Qt Group
Maaret Pyhäjärvi, Director of Consulting at CGI
Felix Kortmann, CTO at Ignite by FORVIA HELLA
Read more: A Practical Guide to Generating Squish Test Scripts with AI Assistants
