investigate

Automated issue diagnosis works by proposing hypotheses, running experiments to test them, and generating a diagnostic report with recommended fixes. This streamlines debugging workflows by refining probabilities based on experiment results without applying changes.

Experiment-based analysis validates hypotheses by running targeted scripts, interpreting the results, and refining the probability of each potential root cause. This methodical approach ensures accurate issue diagnosis before proposing any fixes.

Yes, the root cause analysis process produces a diagnostic report containing recommended fixes without applying them. This allows you to review the proposed solutions for your software issue safely before making any changes.

To identify a root cause, the diagnostic process proposes potential hypotheses, executes experiments to validate them, and interprets the results. It then narrows down the failing behavior to a specific root cause and generates a report with a proposed fix.

Yes, autonomous issue diagnosis requires the ability to execute scripts and interpret their results. This experiment-based analysis validates hypotheses and refines root cause probabilities dynamically during the debugging workflow.

Automated diagnostic reports are ideal when you encounter a failing behavior and need to quickly identify the root cause. It saves time and reduces debugging complexity by autonomously proposing hypotheses and testing them.