mlsanityData to 7 Oct 2026

WebApp1K: A Practical Code-Generation Benchmark for Web App Development

arXiv 2408.00019 ↗First posted 30 Jul 2024cs.SE cs.AIcited by 10

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Abstract (arXiv)

We introduce WebApp1K, a practical code-generation benchmark to measure LLM ability to develop web apps. This benchmark aims to calibrate LLM output and aid the models to progressively improve code correctness and functionality. The benchmark is lightweight and easy to run. We present the initial version of WebApp1K, and share our findings of running the benchmark against the latest frontier LLMs. First, open source LLMs deliver impressive performance, closely trailing behind GPT-4o and Claude 3.5. Second, model size has strong correlation with code correctness. Third, no prompting techniques have been found to lift performance either universally to all models, or significantly to a single model.

Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). Builds on 6 of its 12 references that are in the map. First posted on arXiv 30 Jul 2024.

Builds on

6 of 12 references.

References · 6

  1. 2021Evaluating Large Language Models Trained on Codecited by 8,053
  2. 2021Program Synthesis with Large Language Modelscited by 3,509
  3. 2023SWE-bench: Can Language Models Resolve Real-World GitHub Issues?cited by 3,193
  4. 2024A Survey on Large Language Models for Code Generationcited by 646
  5. 2022DS-1000: A Natural and Reliable Benchmark for Data Science Code Generationcited by 442
  6. 2023ClassEval: A Manually-Crafted Benchmark for Evaluating LLMs on Class-level Code Generationcited by 166

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 2; 2025: 4; 2026: 4.

10 papers.