The candidate had aced the algorithm round. Optimal solution, clean code, great communication. She reversed the linked list efficiently, explained her time-space tradeoffs clearly, and answered follow-up questions with confidence.
Then came the debugging interview.
The interviewer shared a screen showing a small service—maybe 400 lines of Python—that was returning wrong results for certain inputs. "Find the bug," he said. "Talk me through your process."
She stared at the code. She scrolled up and down. She asked no questions. After ten minutes of silence punctuated by random clicks, she said, "I'm not really sure where to start."
She didn't get the offer.
This pattern is common enough that it deserves attention. Many engineers who perform brilliantly on algorithm challenges struggle when faced with real debugging tasks. They can construct elegant solutions from scratch but can't navigate existing code, form hypotheses about failures, or systematically isolate problems.
Yet debugging is what engineers actually do. Studies suggest developers spend 35-50% of their time reading code rather than writing it, and much of that reading happens in the context of fixing problems[^1]. A technical interview that doesn't test debugging misses half the job.
After implementing debugging rounds at dozens of companies through SmithSpektrum, I've learned what makes them effective—and what makes them frustrating exercises in watching candidates flounder.
Why Traditional Coding Interviews Miss This
Standard coding interviews assess construction skills. Given a problem, can you design and build a solution? This matters—you need engineers who can write good code. But it's incomplete.
Construction skills and debugging skills overlap but aren't identical. Construction requires algorithm knowledge, language proficiency, and the ability to translate requirements into code. Debugging requires reading comprehension of unfamiliar code, hypothesis formation about unknown failures, systematic investigation skills, and tool proficiency with debuggers and logs.
Some engineers have both. Many have one but not the other. I've seen senior engineers with fifteen years of experience who could debug anything but struggled to pass LeetCode-style interviews. I've seen new graduates who crushed every algorithm challenge but were helpless when faced with a production issue in an unfamiliar codebase.
The correlation between these skill sets is weaker than most hiring processes assume. By testing only construction, you're selecting for a specific skill set that may not predict real-world effectiveness.
Debugging interviews fill this gap. They put candidates in the position they'll actually be in on day one: here's code you didn't write, it's not working right, figure out why.
Designing Good Debugging Problems
The best debugging problems feel like realistic work: a manageable codebase with a specific, findable bug that rewards systematic investigation.
Size matters. Too small—under 100 lines—and the bug is obvious by inspection. Too large—over 600 lines—and candidates spend all their time just understanding the code. The sweet spot is 200-500 lines: big enough that candidates can't hold it all in their head, small enough that they can form a mental model within the interview time.
| Bug Type | What It Tests | Good Candidate Response | Red Flag |
|---|---|---|---|
| Logic error | Systematic thinking | Traces execution step-by-step | Random changes hoping to fix |
| Race condition | Concurrency understanding | Identifies shared state | Doesn't consider threading |
| Memory leak | Systems knowledge | Checks resource lifecycle | Only looks at logic |
| Integration bug | End-to-end thinking | Checks boundaries and contracts | Assumes components work |
| Performance issue | Profiling skills | Measures before changing | Optimizes randomly |
Use languages candidates know. Debugging tests debugging skills, not language familiarity. If a candidate knows Python best, give them Python. If they know Go, give them Go. Don't make them debug in a language they're unfamiliar with—you'll conflate debugging ability with syntax knowledge.
One bug is usually enough. Some companies seed multiple bugs, thinking more bugs means more signal. In practice, it creates chaos. Candidates find one bug, aren't sure if they're done, waste time looking for more. One clear bug with perhaps one minor secondary issue is plenty.
The bug should be reproducible. Nothing is more frustrating than a flaky reproduction. When candidates trigger the bug reliably, they can iterate hypotheses efficiently. When it appears intermittently, they spend interview time waiting for it to manifest.
The bug should reward investigation, not tricks. Good debugging bugs have multiple valid investigation paths—you could find it through logs, through debugging, through careful code reading, through systematic bisection. Bad debugging bugs require knowing one specific obscure thing—an edge case in a library, a subtle language gotcha. The former tests debugging; the latter tests trivia.
Types of Bugs That Work
Different bug types test different skills, and the right choice depends on what you're assessing.
Logic errors are the bread and butter of debugging interviews. The code compiles, runs, produces wrong results for certain inputs. Finding these requires reading code carefully, understanding intent, and identifying where execution diverges from expectation. They're medium difficulty and test core debugging skills.
Off-by-one errors are simpler but still valuable. They test attention to detail and careful analysis of boundary conditions. Good for more junior candidates or as a warm-up.
Race conditions are hard but extremely valuable for positions involving concurrency. The bug manifests intermittently, and finding it requires understanding concurrent execution. If your work involves threading, async operations, or distributed systems, this reveals crucial skills.
Configuration errors test holistic thinking. The code is fine; something in the environment is wrong. These test whether candidates think systemically or tunnel-vision on code.
Integration failures test understanding of system boundaries. The bug is in how two components interact, not in either component alone. Good for senior roles where systems thinking matters.
Performance issues are a different beast—the code works but is slow. These test profiling skills and optimization thinking. Useful for performance-sensitive roles.
The Interview Structure
A good debugging interview has a clear flow: setup, investigation, discussion.
Start with five minutes of setup. Introduce the problem context: what this code does, what the symptom is, what constitutes success. Make sure the candidate can reproduce the bug and has their environment working. Technical issues in this phase aren't the candidate's fault—help them past these quickly.
The investigation phase is the meat of the interview, typically 25-40 minutes. The candidate debugs while you observe. Your role here is subtle: you're not guiding, you're watching. Note their approach: Do they form hypotheses? Do they investigate systematically? Do they use appropriate tools? Do they communicate their thinking?
You can clarify requirements or confirm reproduction if asked, but don't hint at the solution. If a candidate asks, "Is the bug in the authorization module?" the answer is, "I'm not going to tell you where it is—keep investigating." The skill you're testing includes figuring out where to look.
Hints should be rare and reserved for complete stuckness. If a candidate has made no progress for five or more minutes and seems lost rather than systematically working through options, a gentle redirect might be appropriate: "What hypotheses have you formed? Have you ruled any out?" This prompts without giving away the answer.
End with five to ten minutes of discussion. Even if they found the bug, ask: What else did you consider? How would you prevent this bug class? What would you add to make debugging easier? This reveals depth of understanding beyond the specific exercise.
What to Observe
The debugging interview produces rich signal if you know what to watch for.
Strong candidates form hypotheses quickly. Within minutes of seeing the bug, they have theories: "This could be a timing issue, or maybe the validation is wrong, or the cache might be stale." They then investigate these theories systematically, ruling them out or confirming them.
Strong candidates use tools appropriately. They set breakpoints, examine variables, add logging strategically. They don't just stare at the code hoping for insight; they instrument and measure.
Strong candidates read code carefully. They trace execution paths, understand what the code is trying to do, and identify where behavior diverges from intent. This is distinct from algorithm interviews where you construct code; here you're reading code someone else wrote.
Strong candidates communicate throughout. They narrate their thinking: "I'm checking whether this function ever returns null, because if it does, this dereference would fail." This lets you assess their thought process, not just the outcome.
Strong candidates ask clarifying questions. "Is this supposed to handle empty input?" "What's the expected behavior for this edge case?" Understanding requirements is part of debugging.
Weak candidates make random changes without hypotheses. They flip booleans, change comparisons, add print statements without apparent purpose. They're not investigating; they're hoping to stumble on the answer.
Weak candidates can't read unfamiliar code. They struggle to understand what the code does, can't trace execution, can't explain what functions are supposed to return. This is a fundamental skill gap.
Weak candidates don't use debugging tools. They rely entirely on reading code rather than instrumenting it. Sometimes this works for simple bugs, but it's a limitation for complex issues.
Weak candidates go silent. Whether from anxiety or simply not knowing what to do, they stop communicating. You can't evaluate a process you can't observe.
Evaluation Rubric
I recommend evaluating on several dimensions rather than a simple "found it / didn't find it" binary.
Hypothesis formation: Did they generate reasonable theories about the bug? Did they form them quickly? Did they have multiple hypotheses or just one?
Investigation approach: Was their investigation systematic or scattered? Did they use appropriate tools and techniques? Did they narrow down the problem space efficiently?
Code reading ability: Could they understand the existing code? Could they trace execution paths? Could they identify the gap between intent and behavior?
Tool usage: Did they use debugger, logs, or other tools effectively? Were they comfortable with basic debugging infrastructure?
Communication: Did they explain their thinking? Could you follow their process? Could they articulate what they learned?
Outcome: Did they find the root cause? Did they find a symptom but not the cause? Did they get stuck entirely?
Each dimension matters. A candidate who found the bug through random trial and error isn't necessarily better than one who investigated systematically but ran out of time. Both tell you something useful.
Running Remote Debugging Interviews
Virtual debugging interviews have additional challenges. Video calls make it harder to see the candidate's full setup, and connection issues can derail the exercise.
Environment setup is critical. Don't make candidates set up the project on their own machine—too many things can go wrong. Use a cloud IDE like GitHub Codespaces, Replit, or CodeSandbox where the environment is pre-configured and works consistently.
Screen sharing should show everything relevant. Ask candidates to share their full screen or at least the IDE and any terminal windows. You need to see what they're seeing.
Turn-taking needs to be more explicit. In person, you can observe without interrupting. Remotely, silence can be awkward. Establish norms: "I'll mostly let you work, but if I have a question I'll ask. Feel free to think out loud or ask me questions anytime."
Test the setup before the interview. The five minutes lost to "can you hear me?" and "why isn't the debugger working?" are five minutes not spent evaluating.
Common Implementation Mistakes
Some debugging interviews fail not because the format is wrong but because the execution is flawed.
Bugs that require trivia rather than debugging. If the bug is "Python dicts aren't ordered in this version" or "this library has a known quirk with UTF-8," you're testing knowledge, not skill. Good debugging candidates shouldn't need to know every library edge case.
Insufficient time. Debugging takes time—reading code, forming hypotheses, investigating. If candidates routinely run out of time before making meaningful progress, the problem is too large or the time too short. Adjust until good candidates can typically finish.
Environment issues that waste interview time. If candidates spend ten minutes getting the project to build, that's ten minutes of debugging they didn't get to demonstrate. Front-load the setup work.
Too much interviewer guidance. If you're constantly hinting and redirecting, you're not letting the candidate demonstrate their debugging skills. Resist the urge to help unless they're completely stuck.
Evaluating only on outcome. A candidate who found the bug through random guessing isn't necessarily better than one who investigated systematically but ran out of time. Process matters.
Preparing Candidates
For candidates facing debugging interviews, the preparation is different from algorithm prep.
Practice reading unfamiliar code. Open source projects, coworkers' code, anything you didn't write. The skill of understanding someone else's code is distinct from writing your own, and it improves with practice.
Learn your debugging tools. Whatever IDE you use, learn its debugger: breakpoints, watch expressions, stepping through code. Whatever language you use, know how to add useful logging. These are basic professional skills, but many candidates underuse them.
Develop a systematic approach. When you debug at work, notice your process. Where do you start? How do you form hypotheses? How do you narrow down? Having a conscious methodology helps under interview pressure.
Practice thinking aloud. Debugging interviews require narration, which feels unnatural if you're used to debugging silently. Practice explaining your process as you work through problems.
The candidate who froze in the debugging interview? She was brilliant at algorithms—she'd spent months practicing LeetCode. But she'd never worked on a large codebase or debugged anything she didn't write herself. The algorithm skills were real, but they weren't the whole picture.
The candidate who got the offer instead had a different profile. His algorithm solutions were solid but not spectacular. But when the debugging exercise started, he methodically read the code, formed three hypotheses, eliminated two through logging, and found the root cause in 25 minutes while explaining his reasoning throughout.
That's what engineering actually looks like. Test for it.
References
[^1]: SmithSpektrum interview design consultation, debugging rounds at 40+ companies, 2021-2026. [^2]: Hired.com, "Technical Interview Effectiveness Study," 2024. [^3]: Google re:Work, "Structured Interviewing Guide," updated 2025. [^4]: ACM, "Computing Education Research: Assessment Methods," 2024.
Implementing debugging interviews at your company? Contact SmithSpektrum for interview design and training.
Author: Irvan Smith, Founder & Managing Director at SmithSpektrum