Ask most students what a technical interview tests and they will say algorithms, or a specific language, or "how well you know Python." What it actually tests, far more often than people expect, is whether you can take a piece of code that is producing the wrong answer and work out why. Course certificates almost never show this, because courses are built around code that already works. Debugging only shows up when something is broken, and broken code is exactly what most learning material avoids.
Reproduce first
Before you change a single line, make the bug happen on purpose, reliably, on demand. If you cannot reproduce it, you cannot tell whether your fix worked or whether the bug simply did not show up this time. This step gets skipped constantly because it feels like a delay before the "real" work of fixing something, but it is the real work. A fix you cannot prove reproduces the failure first is a guess wearing a lab coat.
Read the error like evidence
An error message and a stack trace are not an inconvenience standing between you and the fix, they are the fix telling you where to look. Read the actual line the error points to, not just the exception type. Read the line above it, and the function that called it. Most students skim the top of a traceback and start guessing; the better habit is to read it the way you would read a witness statement, slowly, and for what it actually says rather than what you expect it to say.
Bisect the problem
When the cause is not obvious from the error alone, narrow it down methodically rather than guessing at fixes. Comment out half the suspect code and see if the bug survives. Add a print statement at the midpoint of the logic and check whether the values look right there. Use a breakpoint if your tools support one, and step through line by line until the moment the value goes wrong. Each of these does the same job: cutting the search space in half instead of staring at the whole file hoping the answer jumps out.
Check your assumptions at the boundaries
Most real bugs live at edges, not in the middle of normal cases, because the middle is what everyone tests by hand while building the thing. Before you accept that your fix is correct, check the boundaries deliberately:
- Types. Is that variable actually the type you think it is, or a string that looks like a number?
- Units. Is this value in seconds or milliseconds, metres or millimetres? Mismatched units produce answers that are wrong by a suspiciously exact factor.
- Off-by-one. Does your loop or slice include or exclude the boundary element the way you intended?
- Timezones. Is that timestamp in UTC or local time, and does the code downstream assume the other one?
A huge share of "mysterious" bugs are one of these four, dressed up as something more complicated.
Fix the cause, not the symptom
It is tempting, once you find the line where a value goes wrong, to patch it there, wrap it in a condition that hides the bad case, and move on. Resist that. Trace back to where the bad value actually originated, and fix it there instead. A patch at the symptom tends to resurface somewhere else later, usually at a worse time, because the actual cause is still sitting in the code, waiting for the next input that triggers it differently.
Prove the fix with a test
A fix you cannot prove is a fix you are hoping about. Write a test that reproduces the original failure, confirm it fails against the old code, then confirm it passes against your fix. This is the step that turns "I think I fixed it" into "I can show you it is fixed," which is precisely the difference a hiring manager or a mentor in your first weeks on the job is looking for.
Why AI agents make this more valuable, not less
There is a temptation to think debugging matters less now that AI coding agents can write large chunks of code for you. It is the opposite. An agent that plans, writes and tests code across a repository will still produce changes that look plausible and are wrong, confidently. Someone has to catch that, and it will not be the agent. The person who can reproduce, read the error, bisect, check the boundaries and prove the fix is the person who can actually work alongside an AI agent rather than just accept whatever it hands over. That is a more valuable skill now than it was two years ago, not a less relevant one.
Where to practise this for real
Reading about debugging and actually doing it under pressure, on code you did not write, are different experiences. A beginner-friendly mission built around a legacy repository with a genuine bug and a real grade at the end gives you exactly that practice, with a scorecard afterwards that shows precisely where your process held up and where it did not.
If you want to see exactly how that scorecard gets built, here is how ProoV evaluates your project.
