bytedance

web-bench

AI#LLM#Benchmark#Code Generation#Web Development #Docker

272

// summary

Web-Bench is a comprehensive benchmark designed to evaluate how effectively large language models handle real-world web development tasks. It consists of 50 complex projects featuring sequential dependencies that simulate professional engineering workflows. The benchmark provides a challenging environment where even state-of-the-art models currently demonstrate significant room for improvement.

// technical analysis

Web-Bench is a specialized benchmark designed to evaluate Large Language Models on their ability to perform complex, multi-step web development tasks that simulate real-world engineering workflows. By utilizing 50 projects with sequential dependencies created by experienced engineers, it addresses the saturation of existing benchmarks like HumanEval and MBPP by providing a significantly more challenging environment. The project prioritizes foundational web standards and framework proficiency, offering a rigorous metric for assessing AI code generation capabilities in professional development contexts.

// key highlights

Features 50 distinct web development projects, each containing 20 sequential tasks to simulate realistic, multi-step coding workflows.

Covers a broad spectrum of web development by focusing on both fundamental web standards and modern web frameworks.

Provides a high-difficulty evaluation environment where even state-of-the-art models like Claude 3.7 Sonnet achieve a low Pass@1 rate of 25.1%.

Offers a more challenging alternative to existing benchmarks like SWE-bench, helping to identify the true limits of current LLM code generation.

Includes a comprehensive leaderboard and dataset hosted on Hugging Face to facilitate transparent model performance comparisons.

Supports containerized evaluation via Docker, ensuring consistent and reproducible testing environments for different LLM configurations.

// use cases

Evaluating LLM performance on complex, multi-step web development tasks

Benchmarking code generation capabilities against real-world web standards and frameworks

Assessing model proficiency in sequential project feature implementation

// getting started

To begin, create a directory containing a config.json5 file to specify your target models and a docker-compose.yml file to configure the environment. Populate the docker-compose file with your necessary API keys and mount the configuration to the provided Docker image. Finally, execute 'docker compose up' to run the evaluation and generate reports in your local directory.