What types of applications or systems have shown the most benefit from the AI-driven features of qTest Copilot during its beta phase?
Applications and systems requiring comprehensive test coverage and efficient test case generation have benefited most. Feedback from the beta program highlighted that qTest Copilot enables users to create complex test cases quickly while identifying gaps in test coverage that might otherwise have been overlooked, making it particularly useful for teams working on high-stakes or complex software systems.
What role does AI play in standardizing test case descriptions, and how does this benefit cross-functional teams?
The generative AI features in qTest Copilot enable more consistent and common test case descriptions. This standardisation simplifies collaboration across cross-functional teams by creating a shared language and framework for test cases, reducing confusion and improving onboarding for new team members. It also ensures uniformity across the entire testing scope, making it easier to manage and maintain, in addition to driving clarity and helping with organisation, especially as customers’ test libraries grow.
Could you explain how the upcoming “test case discovery” feature will help map requirements to existing test cases and reduce redundant testing?
The test case discovery feature, slated for 2025, will map requirements to existing test cases by analysing the relationships between them. This functionality will help teams identify and leverage already existing test assets, minimising the need for duplicative work and ensuring better resource utilisation — reducing time test managers spend organising their test libraries. By improving visibility into coverage, it also helps address gaps and optimise the overall testing process.
How do you foresee the innovation influencing the broader trend of AI adoption within QA and testing environments?
Innovations like qTest Copilot are expected to accelerate AI adoption in QA and testing by demonstrating measurable benefits such as time savings, increased test coverage, and improved quality. As teams see the potential for AI to automate repetitive tasks and identify risks earlier, it is likely to encourage broader acceptance of AI as a key enabler for digital transformation. Furthermore, the emphasis on human-AI collaboration will help mitigate concerns about reliability and control.
What challenges do you anticipate QA teams might face when integrating AI-driven test automation?
QA teams will first need to build trust in AI-driven processes, which requires robust human oversight to validate results and address uncertainties. Secondly, teams may need to address skills gaps, potentially upskilling to effectively guide and review AI outputs. Additionally, as compliance and security concerns continue to take center stage in the evolving world of AI and technological advancements, AI providers need to value data privacy and ensure they are ethically leveraging customer data. Finally, teams will need to overcome integration complexity, particularly in aligning AI-driven automation with existing workflows and tools. Overcoming these challenges will require clear guidelines, continuous iteration, and fostering collaboration between AI systems and human testers.
Mav Turner is the Chief Product and Strategy Officer at Tricentis, a global leader in continuous testing. In his role, Mav oversees research and development as well as product growth and strategy aimed at enabling organizations to accelerate their digital transformation by increasing software release speed, reducing costs, and improving software quality. Prior to joining Tricentis, Mav worked as Vice President of Product at N-able and SolarWinds. Mav holds a Bachelor of Science in Computer Science from Texas State University.
