The evaluation landscape for Large Language Models in software development has undergone dramatic transformation since SWE-bench's introduction in 2023. Performance metrics have exploded from 1.96% initial resolution rates to over 70% on certain variants, representing one of the most rapid capability progressions in AI benchmarking history. However, recent critical analysis reveals fundamental methodological flaws that significantly inflate these performance claims, while new evaluation paradigms emerge to address these limitations.
This technical analysis examines the current state of LLM coding evaluation through six critical dimensions: core methodologies, extended standards, alternative frameworks, performance benchmarks, technical limitations, and emerging trends. The findings illuminate both remarkable progress and substantial evaluation challenges that impact enterprise deployment strategies.