LLM Code Correctness and Code Quality Are Nearly Uncorrelated in C#, Pearson r = 0.075 Across 340 Solutions

arXiv 2608.22529·high signalXBlueskyLinkedInCopy link

An automated framework evaluated GPT, Gemini, Claude, and Grok on 85 algorithmic C# tasks derived from HumanEval, generating 340 solutions scored on three independent axes: functional correctness via unit tests, static quality via Roslyn AST analysis, and runtime efficiency via adversarial BenchmarkDotNet profiling. Correctness and quality attributes correlated at Pearson r = 0.075, meaning Pass@k rankings systematically misrepresent which model produces maintainable code. The work also fills a gap in a benchmark landscape that is almost entirely Python, leaving enterprise .NET without dedicated evaluation.