Insights from Benchmarking Frontier Language Models on Web App Code Generation

TL;DR

This paper evaluates 16 large language models on web app code generation, revealing that model performance varies mainly by mistake frequency, with limited improvements from prompt engineering, emphasizing the need for reliability enhancements.

Contribution

It provides a comprehensive benchmarking of frontier LLMs on web app coding, highlighting the importance of reducing errors and improving model reliability.

Findings

Models have similar knowledge but differ in mistake frequency.

Writing correct code is more complex than generating incorrect code.

Prompt engineering has limited impact on error reduction.

Abstract

This paper presents insights from evaluating 16 frontier large language models (LLMs) on the WebApp1K benchmark, a test suite designed to assess the ability of LLMs to generate web application code. The results reveal that while all models possess similar underlying knowledge, their performance is differentiated by the frequency of mistakes they make. By analyzing lines of code (LOC) and failure distributions, we find that writing correct code is more complex than generating incorrect code. Furthermore, prompt engineering shows limited efficacy in reducing errors beyond specific cases. These findings suggest that further advancements in coding LLM should emphasize on model reliability and mistake minimization.