Why AI Slows Debugging 20%

Source: Dev.to

Introduction

AI coding assistants have changed how we write software—autocomplete, boilerplate generation, refactors—making development faster than ever.
But when it comes to debugging, research groups have found a counterintuitive effect: AI speeds up writing code… but slows down fixing it. This isn’t speculation; it appears in data from METR, Microsoft Research, and Google’s developer studies. The explanation reveals something fundamental about how debugging actually works.

METR Study (2025)

METR’s 2025 study examined professional developers solving real GitHub issues in large codebases.

Key findings

• Coding time: decreased
• Debugging time: increased by ~20%

Why?

Developers spent more time:

• Reviewing AI‑generated code
• Hunting down logic errors that looked correct
• Debugging fixes that didn’t match runtime behavior
• Refactoring mismatched patterns

Example cited by METR

// JavaScript
const filtered = items;

The code looks perfect but fails instantly when item.data is null for a particular case. The point is that AI doesn’t see what actually happened at runtime; it only sees static code.

Microsoft Study

Microsoft tested AI tools on 200 real enterprise bugs.

Additional insight

• 22% of AI‑generated fixes introduced regressions.
• Almost every failure traced back to the same reason: LLMs inferred behavior from static text rather than reasoning from runtime state.

Google Study

Google investigated how developers use AI during debugging. The majority of time was spent not fixing bugs, but reconstructing the situation for the AI:

• 15% – explaining context
• 23% – recreating what happened in DevTools
• 18% – trying AI fixes that didn’t work
• 12% – rolling back bad patches

Overall: ~70% of debugging time was spent compensating for the AI’s lack of runtime visibility.

Runtime vs. Static Context

When debugging, developers rely on:

• Actual variable values
• DOM snapshots
• Event sequences
• Network timing
• Component state
• The moment the UI broke

AI assistants typically receive only:

• A stack trace
• A file
• The error message
• Whatever context the developer types

They do not receive:

• The DOM
• State history
• Render order
• Unresolved promises
• Timing edges
• User input sequence
• Network conditions

Debugging fundamentally lives in runtime behavior, while LLMs operate on static text. This mismatch is the core of the paradox.

theORQL Team Perspective

Our work at theORQL focuses on runtime‑aware debugging, specifically understanding why AI struggles without the context developers automatically obtain from DevTools. We’re not claiming AI is bad at debugging; the research shows a simpler truth: AI debugs poorly because it can’t see what actually happened.

A frontend developer on Dev.to recently wrote about how access to runtime context dramatically reduced her debugging time (link to post). This real‑world experience aligns closely with the research findings—visibility is the difference maker.

Our goal is to explore tools that bridge the divide between runtime context and editor context. We don’t plan to replace debugging, but to give AI the situational awareness humans already rely on.

Community Questions

• Have you seen debugging get slower with AI?
• Do you feel the “visibility gap” when using AI to fix bugs?
• What does an ideal debugging workflow look like to you?

This paradox affects anyone writing frontend code. As debugging becomes increasingly entangled with AI, the community’s perspective will shape what the next decade of tooling looks like.