ClassEval: A Manually-Crafted Benchmark for Evaluating LLMs on Class-level Code Generation

Coding

Evaluates LLMs on class-level code generation with 100 tasks constructed over 500 person-hours. The study shows that LLMs perform worse on class-level tasks compared to method-level tasks.

Contributed By

@zhenningdavidliu

Code

src/inspect_evals/class_eval

Paper

https://arxiv.org/abs/2308.01861

Overview

ClassEval is a benchmark for evaluating large language models (LLMs) on class-level code generation tasks. It contains 100 class-level Python code generation tasks, constructed over approximately 500 person-hours.

Usage

First, install the inspect_ai and inspect_evals Python packages with:

pip install inspect_ai
pip install git+https://github.com/UKGovernmentBEIS/inspect_evals

Then, evaluate against one or more models with:

inspect eval inspect_evals/class_eval --model openai/gpt-4o

After running evaluations, you can view their logs using the inspect view command:

inspect view

If you don’t want to specify the --model each time you run an evaluation, create a .env configuration file in your working directory that defines the INSPECT_EVAL_MODEL environment variable along with your API key. For example:

INSPECT_EVAL_MODEL=anthropic/claude-3-5-sonnet-20240620
ANTHROPIC_API_KEY=<anthropic-api-key>

[!NOTE] The bash and python code is executed inside a Docker container, so you will need to install Docker Engine in order to run the evaluation.

Docker containers are also used in some challenges for auxiliary services. When you run this, the containers are pulled from a public registry. The source code is still included, however, both for your reference and because some files are copied from them into the agent’s container at runtime.

Options

You can control a variety of options from the command line. For example:

inspect eval inspect_evals/class_eval --limit 10
inspect eval inspect_evals/class_eval --max-connections 10
inspect eval inspect_evals/class_eval --temperature 0.5

See inspect eval --help for all available options.

Dataset

See https://huggingface.co/datasets/FudanSELab/ClassEval for more information.

Scoring

For each sample, the LLM-generated code is appended with test cases from the dataset, and the code is executed to check if it passes all the test cases. If all test cases pass, the sample is considered correct, and the score for the sample is 1. Otherwise, the score for the sample is 0.

The final score for the model is the average score across all samples.