Improving test coverage is something every QA team strives for, but achieving it without overwhelming testers is a constant challenge. This is where modern tools like an AI test generator come into play. Instead of spending hours manually identifying gaps, writing repetitive tests, and maintaining outdated test cases, QA teams can now rely on AI-driven workflows that automate much of this heavy lifting.
One of the biggest advantages of using an AI test generator is its ability to analyze application behavior, user interactions, and system logs to automatically suggest or generate missing tests. This means your team can uncover edge cases or rarely used flows that would normally go unnoticed. The result is increased test coverage without asking QA engineers to work late nights or duplicate manual efforts. AI fills the gaps proactively, making the testing process both smarter and more efficient.
Tools like Keploy are taking this a step further. Keploy can automatically convert real user traffic into high-quality test cases, giving teams instant coverage based on how the application is actually being used. This not only increases reliability but also ensures that tests remain relevant even as the application evolves. Integrating a tool like Keploy with your existing AI test generator can create a powerful ecosystem where tests are continuously generated, updated, and validated with minimal human intervention.
Of course, QA professionals still play a crucial role. AI doesn’t replace testers—it supports them by reducing repetitive work and allowing them to focus on exploratory testing, usability, and complex problem-solving. With AI handling the groundwork, QA teams can operate more strategically.
In the end, improving test coverage doesn’t have to mean increasing workload. By leveraging an AI test generator alongside tools like Keploy, teams can accelerate testing, boost accuracy, and maintain strong quality standards—all without burning out their QA engineers.