Course Objective & Scope
Pasting an error message into an LLM and applying the first suggested patch works for trivial syntax mistakes, but in race conditions, state corruption or distributed-system bugs it often masks symptoms and introduces deeper failures. This course instils a scientific, evidence-first debugging workflow powered by coding agents.
Separating symptom from root cause, generating and falsifying competing hypotheses, feeding structured logs/traces/dumps into context, writing a failing reproduction test before touching production code, automating git bisect, and authoring root-cause analysis (RCA) summaries are practised on realistic bug scenarios.
Who Should Attend?
- Software developers and senior engineers
- Engineers handling L3 production escalations and on-call debugging
- Tech leads and QA/SDET engineers
- Prerequisite: active software development experience and familiarity with debuggers and application logs
Key Takeaways
- Formulate competing hypotheses and define falsifiable evidence checks before changing any code
- Analyse stack traces, structured logs and distributed traces while redacting sensitive data
- Construct a minimal reproducible example (MRE) and a failing regression test that proves root cause
- Pinpoint the exact offending commit within minutes using automated
git bisectscripts - Document the fix and systemic prevention steps in a clear post-mortem / RCA note
Curriculum
Guess-and-Patch vs. Evidence-Based Debugging
- Why LLMs default to premature symptom patching
- The scientific debugging loop: Observe → Hypothesise → Instrument/Test → Prove → Fix
Gathering Evidence: Logs, Stack Traces and Runtime State
- Correlating multi-service logs, OpenTelemetry traces and database query plans
- Adding targeted diagnostic instrumentation when initial evidence is insufficient
Minimal Reproducible Examples (MREs) and Regression Tests
- Isolating production failures into deterministic minimal reproductions
- Enforcing the rule of a failing automated test before writing any fix
Git Bisect, Diff Analysis and Hard Bug Classes
- Pinpointing regression commits automatically with
git bisect runand test scripts - Debugging race conditions, async timing, memory leaks and cache invalidation bugs
Root-Cause Analysis (RCA) and Systemic Prevention
- Scanning the codebase for sibling occurrences of the same root defect
- Writing blameless post-mortems and turning lessons into lint rules or regression checks
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