AI in Software Testing
AI in software testing refers broadly to the use of machine learning and generative AI to assist or automate testing activities — generating test cases, healing broken locators, evaluating outputs a simple assertion can't judge, and exploring applications autonomously — a fast-growing set of techniques reshaping how testing gets done.
This is the umbrella category most of the other AI-in-testing terms in this glossary fall under — from narrow, well-established applications like self-healing locators and AI-generated test cases, to newer, more autonomous approaches like agentic testing and LLM-as-a-judge evaluation.
The common thread across all of it is using AI to handle testing tasks that are either too tedious for humans to do exhaustively (generating broad test case coverage) or too subjective for a simple assertion to judge (evaluating whether a generated response is genuinely good) — augmenting testers' judgment rather than replacing the need for it entirely.
