Augmented Testing
Augmented Testing refers to the practice of integrating advanced technologies, particularly Artificial Intelligence (AI) and Machine Learning (ML), into traditional software testing methodologies. It is not full automation, but rather an enhancement where intelligent tools assist human testers to perform tasks faster, more comprehensively, and with greater insight than manual methods alone.
In today's complex software environments, manual testing struggles to keep pace with rapid development cycles and intricate application states. Augmented Testing addresses this by allowing teams to cover more test scenarios, detect subtle defects earlier, and focus human effort on complex exploratory testing rather than repetitive regression checks.
The process typically involves feeding historical test data, application logs, and requirements into ML models. These models then learn patterns of failure, predict high-risk areas, and automatically generate or prioritize test cases. AI tools can observe user behavior in staging environments to identify usability bottlenecks that traditional scripts might miss.
