Short, fact-focused answers to the AI questions software testers ask most often.
AI can draft test cases, suggest edge conditions, analyze logs for failure patterns, recommend assertions, and help maintain scripts with self-healing locators. For log/trace context, see OpenTelemetry.
No. Exploratory testing, usability, accessibility, and domain nuance still require human judgment. Helpful references: Nielsen Norman Group on usability and WCAG for accessibility.
Good for simple flows, less reliable for complex state/async or brittle selectors—review and refactor. Strengthen traces with Playwright Trace Viewer and assertions with Jest matchers or pytest asserts.
Common issues: hallucination/incorrect output, flaky auto-generated tests, opaque reasoning, data privacy, vendor lock-in, and integration. Mitigate with the NIST AI RMF and threat models like OWASP Top 10 for LLM Apps.
Begin with small wins: draft test ideas, generate boilerplate steps, summarize logs, convert requirements to tests—then validate. For reproducible experiments and artifacts, use MLflow or lightweight experiment logs in your CI.
Teams use GPT-style assistants and code completion like GitHub Copilot, plus AI-enabled test platforms and log analyzers. Keep outputs in VCS (Git) and review like any PR.
Yes. Framework design, assertions, mocking, and CI fundamentals make you effective with or without AI. Practical paths: Selenium / Playwright, API tests with Postman → code via pytest or REST Assured, and CI in GitHub Actions.
Combine standard tests with evals for quality, bias, robustness, prompt resilience, and safety. Use evaluation sets and track drift. See NIST AI RMF guidance and dataset curation ideas from Hugging Face Datasets.
It’s trending upward. Skills in prompt design, dataset hygiene, eval design, and risk controls raise your value. Starter resources: Google ML Crash Course and scikit-learn.