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Systematic root-cause analysis — stop guessing, start isolating
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Basado en la clasificación ocupacional SOC
| name | debugging-and-error-recovery |
| description | Systematic root-cause analysis — stop guessing, start isolating |
| difficulty | junior |
| domains | ["general"] |
Debugging is a skill, not luck. Effective debugging is systematic: form a hypothesis, design a minimal test, validate or falsify, repeat. Random changes are not debugging — they are gambling.
Before investigating: reproduce the issue consistently. If you can't reproduce it, you can't verify you've fixed it.
Read the full error message (not just the last line). Check: logs, stack traces, metrics, recent changes. What changed recently?
Write down a specific, falsifiable hypothesis: "I believe the error is caused by X because of Y."
Create the simplest possible test case that reproduces the issue. Remove everything that is not necessary for the bug to manifest.
Design an experiment that will either confirm or falsify your hypothesis. This often means: add a log statement, write a unit test, or add an assertion.
If the evidence falsifies your hypothesis: form a new hypothesis. If it confirms: find the root cause (not just the symptom).
Fix the root cause, not the symptom. Verify the fix: the minimal reproduction must no longer reproduce. The full test suite must pass.
Write a test that would have caught this bug. Add it to the test suite.
"I'll try changing this and see if it works" This is not debugging. Form a hypothesis first.
"The bug is intermittent — I can't reproduce it" Intermittent bugs have causes. Add logging and wait for the next occurrence. Or: analyze the conditions under which it occurred and stress test those conditions.
