A bug report lands. Something is broken. A user expected one thing and got another.

This is the ending of a story. Our job is to figure out how we got here.

The Crime Scene

Every bug is a mystery. Something happened that shouldn’t have, or something didn’t happen that should have. The evidence is scattered across logs, stack traces, and user reports.

Like any good detective, I start by establishing the facts:

  • What exactly was observed? (Not what they think happened - what they actually saw)
  • When did it happen?
  • What was the state of the system?
  • Is it reproducible?

The temptation is to immediately form a theory and start looking for confirmation. This is dangerous. Premature theories blind us to evidence that doesn’t fit.

Working Backward

From the observed symptom, I work backward through the chain of causation.

The error message says X. What could produce that error? There are usually multiple possibilities:

  • Maybe condition A was true when it shouldn’t be
  • Maybe function B returned an unexpected value
  • Maybe resource C was in an invalid state

Each of these becomes a smaller mystery to solve. I pick the most likely and investigate. If it’s a dead end, I backtrack and try another path.

This is where understanding the codebase pays off. The more I know about how the system flows, the better my intuition about where things could go wrong.

The Unreliable Narrator

Here’s something that took me time to learn: user reports are invaluable, but they’re not always accurate.

Not because users lie - they don’t. But because memory is reconstructive, and technical details are easy to misremember.

“I clicked the button and it crashed” might actually mean:

  • They clicked the button, did three other things, then it crashed
  • They clicked a different button that looked similar
  • It didn’t crash - it showed an error message
  • It crashed before they clicked anything, but they didn’t notice until then

I take reports as starting points, not gospel. The actual sequence of events usually reveals itself through logs and reproduction attempts.

The Plot Twist

Some of the most satisfying debugging sessions involve discovering that the bug isn’t where you thought it was.

I once spent hours convinced a calculation was wrong. The numbers just didn’t add up. I traced through the math, checked the formulas, verified the inputs.

The calculation was perfect.

The bug was in how the results were being displayed. A formatting function was truncating decimals before rounding, so 2.45 became 2.4 became 2. The math was right; the storytelling was wrong.

This is why I try to verify my assumptions at every step. The bug is often hiding in the place I was most confident about.

Reproducibility: The Key to Everything

A bug I can reproduce is a bug I can fix. A bug I can’t reproduce is a haunting.

When I can’t reproduce an issue, I gather more information:

  • What environment was the user in?
  • What data were they working with?
  • What had they done in the session before the bug appeared?
  • Is there anything unique about their setup?

Sometimes the reproduction conditions are bizarre. The bug only happens when processing files over a certain size, on Tuesdays, with a specific locale setting. These aren’t truly random - there’s always a reason - but finding the combination can take persistence.

Once I can reproduce it, though, the story starts to tell itself.

The Satisfying Resolution

The best moment in debugging is when everything clicks into place.

You find the line where things go wrong. You understand why they go wrong. And suddenly the entire chain of events makes perfect sense - from root cause to visible symptom.

This is the story revealing itself in its entirety.

Then comes the fix. Ideally, it’s small - a check that should have been there, a condition that was inverted, an edge case that was unhandled. The smaller the fix, the more confidence I have that I’ve found the real problem.

Large fixes make me nervous. They suggest I’m treating symptoms rather than causes, or that my understanding is still incomplete.

What Bugs Teach Us

Every bug is a lesson about the system, about assumptions, about the gap between intention and reality.

Some lessons:

  • That error handling I thought was thorough? There was a case it didn’t cover.
  • That function I assumed would always receive valid input? Someone found a way to call it differently.
  • That concurrent access I thought was synchronized? There was a window of vulnerability.

I try to extract these lessons and apply them going forward. Not just by fixing this bug, but by asking: where else might this pattern occur? What other assumptions might be wrong?

The story of a bug, fully understood, becomes wisdom for preventing the next one.

The Story Never Really Ends

Of course, every fix introduces the possibility of new bugs. Every change to the system changes what can go wrong.

This isn’t pessimism - it’s reality. Software is a living thing, constantly evolving, and debugging is an ongoing conversation with its complexity.

But each bug solved adds to my understanding. Each story told in reverse becomes a story I can tell forward: “Watch out for this pattern. Here’s what can go wrong. Here’s how to prevent it.”

And so the storytelling continues.