Quality Assurance Engineering as a strategic, AI-powered discipline—transforming manual, fragmented testing into scalable, autonomous, and insightful workflows. Their platform is built for enterprise-scale adoption, integrating codeless automation with robust AI, analytics, and governance.
Read MoreThe process of using software tools and scripts to automatically execute test cases, compare actual outcomes with expected results, and report findings without human intervention.
Instead of manually checking each functionality of a software application, test automation enables faster, more reliable, and repeatable testing—especially valuable in agile and DevOps environments where frequent releases are common.
Refers to the structured setup of reusable components, tools, libraries, and best practices that support efficient, scalable, and maintainable automated testing.
Think of it as building the infrastructure or blueprint for test automation—so that you or your team don't need to "reinvent the wheel" every time you write or run a test.
It's a proactive, end-to-end approach to ensure that a software system meets its performance requirements—such as speed, scalability, stability, and responsiveness—before and after deployment.
Unlike performance testing (which checks system behavior under load), performance engineering focuses on designing, developing, testing, tuning, and monitoring software with performance in mind throughout the software development lifecycle (SDLC).
The ability to understand what's happening inside a system by observing its external outputs—such as logs, metrics, and traces.
In simple terms, it helps developers, DevOps, and SRE teams to answer:
● “Is the system healthy?”
● “Why did it slow down or fail?”
● “What exactly happened, and where?”
It's a proactive approach to monitoring and debugging modern, complex, distributed systems—especially in cloud-native environments using microservices, containers, and APIs.
Software is built right from the start—not just tested at the end. Emphasize embedding quality into the development process through AI-powered, scalable, and codeless solutions.
By implementing QA engineering early in the lifecycle, teams can release faster improve coverage, reduce defects, and enhance the performance and stability of applications—especially in complex, enterprise environments.Smarter testing, Better software
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