SWE-bench_Lite

Overview

SWE-bench Lite is a focused subset of SWE-bench containing 300 Issue-Pull Request pairs from 11 popular Python repositories. It provides a more accessible entry point for evaluating automated software engineering capabilities.

Task Description

Task Type: Automated Software Engineering / Bug Fixing
Input: GitHub issue description with repository context
Output: Code patch (diff format) that resolves the issue
Size: 300 carefully selected test instances

Key Features

300 test Issue-Pull Request pairs
11 popular Python repositories covered
Real-world bugs with verified solutions
Evaluation via unit test verification
More manageable than full SWE-bench while still challenging

Evaluation Notes

Requires pip install swebench==4.1.0 before evaluation
Docker images are built/pulled automatically for each repository
See the usage documentation for detailed setup instructions
Popular benchmark variant for initial model comparison

Properties

Property	Value
Benchmark Name	`swe_bench_lite`
Dataset ID	princeton-nlp/SWE-bench_Lite
Paper	N/A
Tags	`Coding`
Metrics	`acc`
Default Shots	0-shot
Evaluation Split	`test`

Data Statistics

Statistics not available.

Sample Example

Sample example not available.

Prompt Template

Prompt Template:

{question}

Extra Parameters

Parameter	Type	Default	Description
`build_docker_images`	`bool`	`True`	Build Docker images locally for each sample.
`pull_remote_images_if_available`	`bool`	`True`	Attempt to pull existing remote Docker images before building.
`inference_dataset_id`	`str`	`princeton-nlp/SWE-bench_oracle`	Oracle dataset ID used to fetch inference context.
`force_arch`	`str`	``	Optionally force the docker images to be pulled/built for a specific architecture. Choices: ['', 'arm64', 'x86_64']
`dockerhub_username`	`str`	`swebench`	DockerHub user/org namespace for remote SWE-bench images.

Usage

Using CLI

evalscope eval \
    --model YOUR_MODEL \
    --api-url OPENAI_API_COMPAT_URL \
    --api-key EMPTY_TOKEN \
    --datasets swe_bench_lite \
    --limit 10  # Remove this line for formal evaluation

Using Python

from evalscope import run_task
from evalscope.config import TaskConfig

task_cfg = TaskConfig(
    model='YOUR_MODEL',
    api_url='OPENAI_API_COMPAT_URL',
    api_key='EMPTY_TOKEN',
    datasets=['swe_bench_lite'],
    dataset_args={
        'swe_bench_lite': {
            # extra_params: {}  # uses default extra parameters
        }
    },
    limit=10,  # Remove this line for formal evaluation
)

run_task(task_cfg=task_cfg)

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