Runs automated end-to-end browser tests on pull requests, posts results as GitHub comments, detects visual and interaction regressions, and captures reproducible traces and screenshots for debugging.
Desktop App for Mac, Windows (PC)
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DebuggAI is an AI-driven end-to-end browser testing and debugging tool that analyzes code changes, runs automated UI tests, and reports results directly in pull requests to help developers find and reproduce front-end issues that unit tests often miss. It inspects diffs, exercises the running app in real browsers, and produces actionable findings such as visual regressions, failing interactions, and reproducible test traces in an integrated workflow.
Key features and capabilities:
Automated browser testing on pull requests: Executes end-to-end browser tests against diffs and posts results as comments on the pull request, giving context where changes were made.
AI-guided test exploration: Uses AI to explore UI surfaces, generate interaction sequences, and uncover edge cases that manual or static tests can overlook.
Reproducible debugging artifacts: Captures deterministic snapshots, input traces, and environment metadata so failures can be replayed and investigated consistently.
Visual regression detection: Compares screenshots and DOM state across runs to identify unintended UI changes and layout regressions.
Integration with developer workflows: Integrates with source control and CI systems to surface test outcomes in code reviews and reduce feedback latency.
Trace and system-level correlation: Produces traces and logs that connect UI failures to underlying system events, aiding triage of complex issues.
Benefits for engineering teams:
Improves visibility into UI-level risks introduced by code changes by combining automated browser runs with AI-driven test generation.
Reduces time to reproduce intermittent or flaky UI bugs through captured snapshots and deterministic replay.
Supplements unit and integration tests by exercising the real application in a browser and surfacing behavioral and visual issues.
Fits into existing review and CI processes, enabling developers to receive contextual test feedback without switching tools.
Technical considerations:
Designed to work with modern web applications and common CI/source-control workflows; capabilities include browser-based interaction recording, screenshot comparison, and content-addressed run artifacts.
Emphasizes reproducibility by capturing environment metadata, deterministic seeds, and replayable test manifests to isolate model variance from environmental flakiness.
Produces machine-readable diagnostics (traces, logs, screenshots) alongside human-readable summaries to support both automated analysis and manual investigation.
Keywords for discoverability: end-to-end browser testing, automated UI tests, visual regression testing, reproducible debugging, pull request testing, AI-driven testing, UI test automation, deterministic replay, test flakiness isolation.
This description focuses on the tool’s functional capabilities and integration points to help teams understand how DebuggAI can fit into development and QA workflows for detecting, reproducing, and diagnosing browser-based UI issues.
This description was generated by AI (artificial intelligence). AI can make mistakes. Check important info.
DebuggAI runs automated browser tests on your pull requests and posts results as GitHub comments. Know your code works before you merge. 2-minute setup, no configuration required.
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