How can automated visual testing detect broken elements after code changes?

Automated visual testing is an essential process for identifying broken UI elements by comparing user-visible changes before and after code updates, using baseline screenshots to detect regressions such as overlapping elements, missing icons, color or font shifts, and layout breaks that might not be discovered by functional tests. By integrating AI-powered filtering and CI/CD automation, this testing minimizes false positives and prevents defects from reaching production, ensuring multi-device consistency. Baseline management is crucial, with approved UI states serving as reference points for comparison, and updates require deliberate QA and developer sign-off. While traditional pixel comparison can be sensitive to rendering variations, AI-powered visual testing reduces noise by prioritizing diffs in key UI regions and normalizing expected variation across environments. Integrating visual testing in CI/CD workflows involves automatic checks on every commit or pull request, with reviewers determining whether changes are intentional or regressions. Techniques such as masking volatile regions, stabilizing data, controlling animations, and isolating components help maintain actionable results, while self-healing locators and AI-powered root cause analysis enhance test stability and facilitate faster triage. Best practices include adding visual checks early in development, prioritizing core user journeys, rigorously versioning baselines, isolating and stabilizing components, executing tests across real environments, and monitoring signal quality to ensure trustworthy results.