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Do LLMs Actually Fix Tricky React Hooks, or Do They Just Cheat?

#devchallenge #kagglechallenge #ai #machinelearning

We've all been there: you ask an AI assistant to fix an ESLint react-hooks/exhaustive-deps warning, and instead of solving the lifecycle issue, it just slaps // eslint-disable-next-line on top and calls it a day. Or worse, it mindlessly dumps an object into the dependency array and sends your component into an infinite re-render loop.

I wanted to scratch this specific developer itch. When models are pushed with real React lifecycle edge cases, do they write idiomatic React, or do they take lazy shortcuts?

What task(s) did you run?

I built a benchmark on Kaggle that tests models against 5 common React hook traps:

  1. The Classic Timer Stale Closure: Testing if the model knows how to use a functional updater (setSeconds(s => s + 1)) instead of resetting the interval every single second.
  2. The Leaked Window Listener: Seeing if it remembers to return an unmount cleanup (removeEventListener) on a resize handler.
  3. The Referential Equality Loop: Handing it an object dependency that triggers an infinite loop to see if it moves it out of the effect or memos it.
  4. The Async Race Condition: Checking if it handles component unmounts during fetch requests using AbortController or active flags.
  5. The Redundant Effect (Derived State): Giving it an effect that updates state when firstName or lastName changes. The modern React answer is to delete the effect entirely and compute it inline.

Every output was graded strictly: points were deducted if the model used eslint-disable, broke runtime behavior, or failed to apply the idiomatic pattern.

Which models did you run it against?

I tested four models across different architectures and sizes:

  • Gemma 4 31B (Google open-weight)
  • Gemini 3.7 Flash (Google API)
  • DeepSeek-R1 (DeepSeek reasoning model)
  • Gemini 2.5 Pro (Google reasoning model)

What are the main insights?

Here is how the leaderboard shook out:

Model Score Pass Rate
Gemma 4 31B 1.00 5 / 5 (100%)
Gemini 3.7 Flash 1.00 5 / 5 (100%)
DeepSeek-R1 0.80 4 / 5 (80%)
Gemini 2.5 Pro 0.80 4 / 5 (80%)

The Most Interesting Discoveries

  • Reasoning models love to over-explain (and it broke the pipeline): DeepSeek-R1 actually understood the problem in Test 5 properly. Its internal thoughts explicitly noted that derived state shouldn't live in a useEffect. However, despite a strict system prompt saying "Output ONLY the refactored code without conversational explanation," it couldn't resist dumping paragraphs of commentary. Because automated parsers look for clean code, this verbosity caused it to fail.
  • The "Zombie Import" bug: Even inside DeepSeek's generated code snippet, it deleted the useEffect call from the component body, but left import { useState, useEffect } from 'react'; untouched at the top. In any strict TypeScript/ESLint setup, that instantly trips an unused variable warning.
  • Flash & smaller models followed negative constraints better: Gemini 3.7 Flash and Gemma 4 31B followed instructions directly. They returned clean code, stripped the unused imports, avoided eslint-disable, and scored a clean 100%.

What I'd Measure Next

Expanding this to React 19 patterns—testing how reliably models migrate legacy useEffect data-fetching code to the new use() hook and Server Actions without hallucinating deprecated patterns.

Where can we see it?

You can inspect the benchmark code, test cases, and outputs on Kaggle here:

https://www.kaggle.com/code/elevendev/react-hook-lifecycle-closure-reliability

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