Bug / Debugging
A bug is an error in software; debugging is the process of finding and fixing it. Modern tools like debuggers, logging and error tracking make finding faults easier.
Every piece of software has bugs – the question is whether they are found before they reach users. Professional debugging is a core skill that separates good teams from bad. From simple typos to complex race conditions, systematic debugging saves time, money and nerves.
What is Bug / Debugging?
A bug is a defect in code that causes software to behave differently than intended. The term is said to come from a moth found in a Harvard computer in 1947. Bugs can be logic errors, syntax errors, off-by-one errors, null references, race conditions, memory leaks or wrong assumptions about input. Debugging is the systematic process of finding, analysing and fixing bugs by forming hypotheses, testing and refining until the cause is found and fixed.
How does Bug / Debugging work?
Systematic debugging: 1) Reproduce – reliably trigger the bug with defined steps. 2) Narrow down – isolate the faulty area (bisecting, logging). 3) Analyse – find the root cause, not just the symptom. 4) Fix – correct the bug without introducing new ones. 5) Verify – confirm the fix and check for side effects. 6) Regression – add an automated test that would have caught the bug. Tools help at each step: debuggers (breakpoints, step-through), logging (structured logs, severity), error tracking (Sentry, Datadog), profilers for performance bugs.
Practical Examples
Null pointer: A function accesses an object that is undefined – very common in JavaScript. TypeScript strict mode reduces this.
Race condition: Two processes access the same data at once and overwrite each other – intermittent and hard to reproduce.
Off-by-one: A loop runs one time too many or too few – classic cause of array overflows or missing rows.
Memory leak: Memory is not freed – performance degrades over hours or days until crash.
Timezone bug: A date is stored in UTC but shown in local time without conversion – common in international apps.
Typical Use Cases
Production: Error tracking (Sentry, Datadog) reports bugs in real time before users do
Code review: Peers check for potential bugs before merge
Automated testing: Unit, integration and E2E tests find bugs before deployment
Legacy rescue: Systems with many bugs are stabilised and cleaned up
Performance: Profilers find bottlenecks in slow applications
Advantages and Disadvantages
Advantages
- Systematic debugging saves time compared to trial and error
- Error tracking finds bugs proactively before users are affected
- Automated tests prevent regressions and lock in fixes
- Good debugging improves understanding of the system
- Structured logging enables fast analysis in production
Disadvantages
- Debugging can be very time-consuming, especially for intermittent or distributed bugs
- Heisenbug: Some bugs disappear when observed (e.g. due to timing changes)
- Root cause analysis needs deep system knowledge and experience
- Over-engineering: Sometimes more time is spent analysing than the bug warrants
Frequently Asked Questions about Bug / Debugging
How do I find the cause of a hard-to-reproduce bug?
What is the difference between a bug and a feature request?
How do I reduce bugs in my software?
Related Terms
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