Debugging: A Systematic Approach
Master a repeatable, systematic process for finding and fixing bugs across any language or environment. By the end, you will have built a personal debugging toolkit and solved real-world bug scenarios using structured techniques.
Course Content
6 modules · 30 lessonsThe Debugging Mindset
Adopt a systematic, hypothesis-driven approach to debugging instead of guessing.
Why random debugging wastes hours and how to stop
debugging definition, reactive vs systematic debugging, cost of guessing, bug lifecycle, developer mindset shift
How to reproduce a bug reliably every time
bug reproduction, minimal reproducible example, environment isolation, flaky bugs, reproduction checklist
How to read an error message without panicking
error message anatomy, stack traces, error types, signal vs noise in errors, first line vs root cause
What is a hypothesis in debugging and how to form one
hypothesis formation, falsifiable hypothesis, debugging hypothesis vs guess, narrowing fault location, one variable at a time
How to keep a debugging log that actually helps
debugging journal, tracking hypotheses, time-boxing, rubber duck debugging, documentation of findings
Reading Code to Find Bugs
Use structured code reading techniques to locate faults without running the program.
How to trace program execution mentally step by step
mental execution model, variable state tracking, execution order, tracing loops, side effects
How boundary conditions cause most logic bugs
boundary conditions, off-by-one errors, empty input, null values, integer overflow, edge case enumeration
How to spot common JavaScript and Python bug patterns
type coercion bugs, mutable default arguments, closure variable capture, async callback order, reference vs value
How to do a code review to find bugs before running code
static code reading, checklist-driven review, assumption identification, precondition checks, defensive reading
How to use git blame and git log to find when a bug was introduced
git blame, git log, git bisect, commit history investigation, blame workflow
Using Debuggers and Dev Tools
Control program execution precisely using breakpoints, watchers, and step controls to observe state at any point.
How breakpoints work and when to use them
breakpoints, conditional breakpoints, execution pause, program state inspection, breakpoint vs console.log
Step over, step into, step out -- when to use each
step over, step into, step out, call stack navigation, function execution control, debugger flow control
How to inspect variables and watch expressions in a debugger
variable inspection, watch expressions, scope panel, hover inspection, evaluating expressions at breakpoints
How to debug asynchronous JavaScript with DevTools
async stack traces, async/await debugging, promise debugging, event listener breakpoints, async call stack
How to use the Network panel to debug API and fetch issues
network panel, request inspection, response headers, status codes, payload inspection, CORS errors
Strategic Logging and Observability
Design and place logs that surface bugs in production where a debugger is unavailable.
Why console.log debugging fails at scale and what replaces it
console.log limitations, structured logging, log levels, production logging, noise vs signal in logs
Where to place logs to trace a bug through your system
log placement strategy, entry and exit logging, function boundaries, error path logging, correlation IDs
How to use error tracking tools like Sentry to catch production bugs
error tracking, Sentry setup, breadcrumbs, error context, alerting, grouping errors
How to debug performance issues using timing logs
performance debugging, timing measurements, bottleneck identification, slow queries, profiling vs timing logs
How to write a log query to diagnose a bug from log output
log querying, filtering logs, grep patterns, JSON log parsing, correlation ID filtering, log analysis workflow
Testing as a Debugging Tool
Write tests that pin down bugs precisely, prevent regressions, and document what the correct behavior is.
How to write a failing test that proves a bug exists
test-driven bug fixing, red-green cycle, failing test as specification, assert-first pattern, test isolation
How to write regression tests so bugs never come back
regression testing, test coverage for bug fixes, commit-linked tests, test naming conventions, prevention vs detection
How to use binary search to isolate a bug in a large codebase
binary search debugging, divide and conquer, code commenting strategy, input bisection, execution path narrowing
How property-based testing finds bugs you never thought to test
property-based testing, invariants, fast-check, fuzz testing, generating edge cases automatically
How to debug flaky tests -- tests that pass and fail randomly
flaky tests, timing dependencies, test isolation failures, shared state, async race conditions in tests, quarantining flaky tests
Debugging in Production and Distributed Systems
Diagnose bugs in live systems where you cannot pause execution, using distributed tracing, feature flags, and rollback strategies.
How to debug a production incident without taking down the site
production debugging, safe investigation, no-breakpoint debugging, read-only investigation, non-invasive debugging techniques
How distributed tracing works and how to use it for debugging
distributed tracing, trace ID, span, OpenTelemetry, trace visualization, latency analysis across services
How to use feature flags to safely test fixes in production
feature flags, gradual rollout, kill switch, canary deployment, flag-based debugging, targeted user testing
How to debug memory leaks in Node.js applications
memory leaks, heap snapshots, garbage collection, event listener leaks, closure retention, V8 memory tools
How to write a postmortem that prevents the bug from recurring
postmortem, blameless culture, five whys, contributing factors, action items, incident timeline