Debugging as a Core Engineering Skill: Why It Matters More Than Ever

Introduction: The Most Undervalued Engineering Skill

When we interview engineers, we obsess over their ability to write code. Can they implement a linked list? Solve a dynamic programming problem? Build a REST API? But there's a skill that matters far more for day-to-day engineering work: the ability to figure out why something isn't working.

Studies consistently show that developers spend 35-50% of their time debugging. Yet traditional interviews spend approximately zero time assessing this skill. We're testing candidates on activities that represent maybe 20% of their job while ignoring the activities that consume half their working hours.

"The best engineer on my team writes half as much code as others but ships twice as fast. The difference? When something breaks, she finds the root cause in minutes while others spend hours."

— VP Engineering, Series B SaaS Company

The AI Debugging Paradox

Here's something counterintuitive: as AI gets better at writing code, debugging skills become more valuable, not less. Why? Because AI-generated code still fails, and when it does, AI often can't figure out why.

Why AI Struggles with Debugging

• Limited Context: AI doesn't know your production environment, data patterns, or system interactions
• No Runtime Visibility: AI can't see actual logs, metrics, or error messages in your specific deployment
• Missing History: AI doesn't know what changed recently or what worked before
• Complex Interactions: Bugs often emerge from unexpected interactions between systems that AI hasn't observed

This creates a paradox: teams using AI heavily actually need better debuggers because they're generating more code faster, which means more potential bugs to diagnose.

The Systematic Debugging Approach

Great debuggers aren't lucky—they're systematic. They follow a repeatable process that consistently leads to root causes. Here's what that looks like:

Step 1: Reproduce the Problem

Before anything else, can you reliably make the bug happen? Inexperienced engineers often skip this step and start changing code randomly. Systematic debuggers invest time in creating a reliable reproduction case.

Step 2: Gather Information

What do you actually know? What error messages appear? What do the logs show? When did this start happening? What changed? Great debuggers resist the urge to form theories until they have data.

Step 3: Form Hypotheses

Based on evidence, generate possible explanations. Strong debuggers typically generate 3-5 hypotheses and rank them by likelihood. They also identify what evidence would confirm or refute each hypothesis.

Step 4: Test Hypotheses

Systematically test each hypothesis, starting with the most likely. This might involve adding logging, reproducing in a controlled environment, or isolating components.

Step 5: Verify the Fix

Once you've found the cause, verify your fix actually resolves the issue. Don't assume—test. And consider whether your fix might break something else.

The Debugging Mindset

Beyond process, great debuggers share certain mental traits:

Intellectual Humility

They assume their mental model might be wrong. When the bug doesn't make sense, they question their assumptions rather than blaming the tools.

Patient Curiosity

They're genuinely curious about why systems behave unexpectedly. This curiosity sustains them through frustrating debugging sessions.

Systematic Thinking

They resist the urge to change things randomly. Every code change has a reason, and they track what they've tried.

Broader Context Awareness

They consider the entire system, not just the code. Could the problem be in configuration? Infrastructure? Dependencies? External services?

Five Debugging Dimensions to Assess

When evaluating debugging ability in interviews, look at these five dimensions:

1. Information Gathering

Does the candidate ask good questions before diving in? Do they seek to understand the context, environment, and recent changes? Or do they immediately start changing code?

2. Hypothesis Formation

Can they generate multiple plausible explanations for what might be wrong? Do they reason about likelihood based on evidence?

3. Systematic Testing

Do they test one variable at a time? Can they design experiments that definitively confirm or refute a hypothesis?

4. Tool Proficiency

Can they read stack traces? Navigate logs? Use debuggers effectively? Understand monitoring dashboards?

5. Root Cause Analysis

Do they stop at the surface symptom or dig to the root cause? A memory leak might be the symptom, but the root cause is the code that holds references incorrectly.

Assessing Debugging in Interviews

Traditional coding interviews completely miss debugging ability. Here's how to assess it:

Bug Hunt Challenges

Present candidates with code that has a subtle bug. Give them access to logs, tests, and the ability to add print statements or use a debugger. Observe their process.

Production Scenarios

Describe a realistic production incident: "Users are reporting slow response times, but only intermittently. Here's what we know..." See how they would approach investigation.

Code Review with Bugs

Give candidates code to review that contains hidden bugs. Can they spot issues through careful reading and reasoning about edge cases?

What to Observe

• Do they reproduce before fixing?
• Do they gather information systematically?
• Can they articulate multiple hypotheses?
• Do they verify their fix actually works?
• Can they explain the root cause clearly?

What Great Debuggers Do Differently

After observing thousands of debugging sessions, we've identified patterns that separate great debuggers from average ones:

They Write Down What They Know

Great debuggers externalize their reasoning. They maintain notes on what they've tried, what they've learned, and what hypotheses remain. This prevents going in circles.

They Question "Impossible" Behaviors

When something seems impossible, they dig deeper instead of giving up. The "impossible" behavior usually reveals a flawed assumption.

They Understand the Stack

They can debug across layers—from application code to framework to operating system to network. They know when to go deeper and when to look elsewhere.

They Build Debugging Infrastructure

Great debuggers invest in making future debugging easier. They add logging where it's missing, build test harnesses, and document non-obvious behaviors.

They Learn from Every Bug

After fixing a bug, they reflect: What made this hard to find? How could we have caught it earlier? What systemic issue allowed this to happen?

Conclusion

In an age where AI can write increasingly sophisticated code, the ability to understand why code fails becomes more valuable, not less. Debugging is the skill that lets engineers maintain, evolve, and operate complex systems in production.

Yet traditional interviews ignore it completely. We spend hours testing algorithm knowledge that candidates will rarely use, while completely ignoring the systematic troubleshooting skills they'll use every day.

The companies that learn to assess debugging ability will hire engineers who ship faster, cause fewer incidents, and resolve production issues in minutes instead of hours. That's a competitive advantage worth pursuing.