How to Find the Root Cause of a Failed Test with AI in Under a Minute

TestMu AI’s AI Root Cause Analysis helps teams investigate failed tests across HyperExecute, Test Manager, KaneAI, and Web or App Automation by analyzing captured logs, screenshots, execution data, and, on HyperExecute, repository git data. It produces an error classification, trend information, a chronological timeline distinguishing root-cause events from downstream effects, and suggested remediation and verification steps, though engineers are expected to validate its conclusions. Its usefulness depends on evidence collected during runs, including command, video, screenshot, network, console, Appium, Playwright trace, and git data, with several detailed log types requiring explicit configuration. RCA can be started manually for individual failures, enabled automatically for all, new, or consistently failing tests using targeting rules, or integrated into CI through public APIs that trigger analyses, monitor progress, and retrieve detailed results. Organizations can tailor classifications and instructions to their own recurring failure patterns, while related Insights capabilities help distinguish flaky, chronic, new, environmental, product, and test-automation failures before analysis. Each analysis consumes credits, typically priced at 15–25 credits, and access requires eligible subscriptions, enabled AI features, and appropriate organization permissions; insufficient credits or disabled capabilities prevent API generation. The platform supports follow-up actions such as filing bugs, rerunning failed scenarios, selectively retrying transient errors, using auto-healing cautiously, or muting known failures, but it does not automatically create external tickets and has product-specific coverage, beta features, and data-capture limitations.

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