That is not a disclaimer — it is part of the point. The slides and the voice narrating the lectures were produced with AI tools, but every script was written and fact-checked by a human instructor with real hands-on experience. Think of the AI as the production crew; the teaching is human. Here’s a number that should stop you. Developers and testers spend roughly half their time not building or testing, but debugging — chasing down why something failed. Half. It’s the most expensive, most frustrating, most dreaded task in all of software. And it just changed. AI turns out to be genuinely brilliant at debugging — reading errors, generating hypotheses, sifting logs, isolating bugs.
What you will learn:
AI for Debugging and Failure Analysis teaches you to turn testing’s most dreaded task into one of its fastest. You’ll take an error or a stack trace and get a clear explanation in seconds. You’ll do root-cause analysis with AI as a hypothesis engine. You’ll sift mountains of logs, reproduce and isolate bugs, and diagnose flaky tests, CI failures, UI failures, performance problems, distributed failures, and even AI-system failures. And you’ll write bug reports that actually get bugs fixed.
What this course covers, module by module:
Why Debugging Is Hard; Reading Errors & Stack Traces; Root-Cause Analysis; Analyzing Logs at Scale; Reproducing & Isolating Bugs; Debugging Test Failures; Failures in CI/CD; Automation & UI Failures; Performance & Resource Failures; Distributed & Integration Failures; AI & ML System Failures; Communicating & Reporting Failures; Pitfalls & Verification.
