AI Testing
// Definition
The use of AI — language models, machine-learning classifiers, and AI-powered platforms — to accelerate testing tasks: generating test code from descriptions, analysing logs and stack traces, suggesting edge cases, healing broken locators, comparing screenshots intelligently, and triaging failures. AI augments QA engineers; it does not replace the judgement, exploration, and risk-modelling work that humans still do best.
// Related terms
Prompt Engineering
The craft of writing inputs to AI tools — language models, chat assistants, coding assistants — so that the output is useful, specific, and aligned with the task. Core principles include being specific about format, providing project context (existing patterns, conventions, examples), asking for chain-of-thought reasoning, enumerating edge cases up front, and iterating across multiple turns rather than expecting a perfect first response.
Self-Healing Tests
Test automation that captures multiple element attributes during recording (id, class, position, text, accessibility role) and uses an AI matcher to find the closest replacement when the original locator stops working. Reduces locator-rot maintenance for minor UI changes but cannot recover from major redesigns and can silently mis-bind to wrong elements — healed selectors require human review. Common implementations include Mabl, Testim, Functionize, and the open-source Healenium.
GitHub Copilot
An AI coding assistant built by GitHub and Microsoft, powered by OpenAI models. It runs as an IDE plugin (VS Code, JetBrains, Visual Studio, Vim) and produces inline code suggestions as you type, plus a chat panel for explanations, fixes, and test generation. Widely adopted by QA engineers for accelerating test authoring; output requires human review for hallucinated APIs and incorrect assertions.
Visual AI Testing
Visual regression testing using an ML model that distinguishes meaningful UI changes (missing elements, layout shifts, broken images, colour regressions) from rendering noise (anti-aliasing, sub-pixel rendering, dynamic timestamps). Compared to pixel-by-pixel diffs, visual AI dramatically reduces false positives — critical for cross-browser and cross-device matrices. Common tools include Applitools Eyes, Percy, and Chromatic.
Learn more · Playwright MCP
Chapter 1 · Lesson 2: Why Playwright MCP — Use Cases for QA