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FEA-Bench

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Auto-converted to Parquet Duplicate
The dataset viewer is not available for this split.
Cannot load the dataset split (in streaming mode) to extract the first rows.
Error code:   StreamingRowsError
Exception:    CastError
Message:      Couldn't cast
instance_id: string
pull_number: int64
repo: string
version: string
base_commit: string
environment_setup_commit: string
created_at: string
FAIL_TO_PASS: list<element: string>
  child 0, element: string
PASS_TO_PASS: list<element: string>
  child 0, element: string
problem_statement: string
docker_image: string
-- schema metadata --
pandas: '{"index_columns": [], "column_indexes": [], "columns": [{"name":' + 1464
to
{'instance_id': Value('string'), 'pull_number': Value('int64'), 'repo': Value('string'), 'version': Value('string'), 'base_commit': Value('string'), 'environment_setup_commit': Value('string'), 'created_at': Value('string'), 'FAIL_TO_PASS': List(Value('string')), 'PASS_TO_PASS': List(Value('string'))}
because column names don't match
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/utils.py", line 99, in get_rows_or_raise
                  return get_rows(
                         ^^^^^^^^^
                File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
                  return func(*args, **kwargs)
                         ^^^^^^^^^^^^^^^^^^^^^
                File "/src/services/worker/src/worker/utils.py", line 77, in get_rows
                  rows_plus_one = list(itertools.islice(ds, rows_max_number + 1))
                                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2690, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2227, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2251, in _iter_arrow
                  for key, pa_table in self.ex_iterable._iter_arrow():
                                       ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 494, in _iter_arrow
                  for key, pa_table in iterator:
                                       ^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 384, in _iter_arrow
                  for key, pa_table in self.generate_tables_fn(**gen_kwags):
                                       ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/parquet/parquet.py", line 209, in _generate_tables
                  yield Key(file_idx, batch_idx), self._cast_table(pa_table)
                                                  ^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/parquet/parquet.py", line 147, in _cast_table
                  pa_table = table_cast(pa_table, self.info.features.arrow_schema)
                             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2321, in table_cast
                  return cast_table_to_schema(table, schema)
                         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2249, in cast_table_to_schema
                  raise CastError(
              datasets.table.CastError: Couldn't cast
              instance_id: string
              pull_number: int64
              repo: string
              version: string
              base_commit: string
              environment_setup_commit: string
              created_at: string
              FAIL_TO_PASS: list<element: string>
                child 0, element: string
              PASS_TO_PASS: list<element: string>
                child 0, element: string
              problem_statement: string
              docker_image: string
              -- schema metadata --
              pandas: '{"index_columns": [], "column_indexes": [], "columns": [{"name":' + 1464
              to
              {'instance_id': Value('string'), 'pull_number': Value('int64'), 'repo': Value('string'), 'version': Value('string'), 'base_commit': Value('string'), 'environment_setup_commit': Value('string'), 'created_at': Value('string'), 'FAIL_TO_PASS': List(Value('string')), 'PASS_TO_PASS': List(Value('string'))}
              because column names don't match

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Dataset Card for FEA-Bench

A Benchmark for Evaluating Repository-Level Code Generation for Feature Implementation.

Dataset Details

Dataset Description

The FEA-Bench is a benchmark with a test set that contains 1,401 task instances from 83 Github repositories. This benchmark aims to evaluate the capabilities of repository-level incremental code development. The task instances are collected from Github pull requests, which have the purpose of new feature implementation. Each task instance includes the repo and the base commit sha256, and the PR number and the status of unit test.

  • Curated by: the authors of the FEA-Bench paper: Wei Li, Xin Zhang, Zhongxin Guo, Shaoguang Mao and their collaborators.
  • Language(s) (NLP): English
  • License: Others; We list all licenses of involved github repositories in the last part.

Uses

This dataset is designed to evaluate performances of LLMs on repository-level code development, which is a complicated software engineering task.

  • Repository-level incremental code development: The FEA-Bench can be used to evaluate a model for the the capabilities of repository-level incremental code development. Success on this task is typically measured by achieving a high/low resolved ratio. The leaderboard will soon be published as a website.

Direct Use

Use scripts from FEA-Bench repo to get info for task instances and organize them into prompt, which can be used to LLMs' inference. Also, you can get info or use agents to directly solve the PRs with code changes.

Out-of-Scope Use

This dataset is not aimed at training for LLMs. You should not take the FEA-Bench as the training dataset to avoid contamination.

Dataset Structure

An example:

{
    "instance_id": "huggingface__accelerate-270",
    "pull_number": 270,
    "repo": "huggingface/accelerate",
    "version": null,
    "base_commit": "515fcca9ed2b36c274c595dbdff75f1c2da635de",
    "environment_setup_commit": "08101b9dde2b1a9658c2e363e3e9f5663ba06073",
    "FAIL_TO_PASS": [
        "tests/test_state_checkpointing.py::CheckpointTest::test_can_resume_training",
        "tests/test_state_checkpointing.py::CheckpointTest::test_invalid_registration",
        "tests/test_state_checkpointing.py::CheckpointTest::test_with_scheduler"
    ],
    "PASS_TO_PASS": []
}

Dataset Creation

Curation Rationale

Implementing new features in repository-level codebases is a crucial application of code generation models. However, current benchmarks lack a dedicated evaluation framework for this capability. To fill this gap, we introduce FEA-Bench, a benchmark designed to assess the ability of large language models (LLMs) to perform incremental development within code repositories.

Source Data

Data Collection and Processing

We collect pull requests from 83 GitHub repositories and use rule-based and intent-based filtering to construct task instances focused on new feature development. Each task instance containing code changes is paired with relevant unit test files to ensure that the solution can be verified.

Who are the source data producers?

Authors of 83 Github repositories list in the last part.

Personal and Sensitive Information

The dataset does not include any personal or sensitive information.

Bias, Risks, and Limitations

  • The quantity of high-quality data suitable for repository-level incremental development is limited. High-quality and usable pull requests for new feature development are relatively scarce. Many repository-level code developments for implementing new functionalities were committed during the early stages of repositories, without going through the rigorous code review process typical of the open-source community, resulting in lower data quality that cannot be utilized.
  • Furthermore, the software's early-stage developments might not even have been conducted using the GitHub platform, posing a challenge for data collection and utilization.
  • The repository-level incremental code development may not just include new feature implementation tasks.
  • Only Python repositories are involved in FEA-Bench.
  • The inference results of the task instances from the benchmark may contain code that is harmful to computer systems.

Recommendations

Evaluation by docker is recommended, just like SWE-bench. We will also publish a patch for SWE-bench to make it compatible for our tasks' evaluation.

BibTeX:

To be appeared after publishing the ArXiv paper.

APA:

To be appeared after publishing the ArXiv paper.

Dataset Card Contact

For further information or questions, please contact Xin Zhang (xinzhang3@microsoft.com).

All involved Github repositories in the FEA-Bench

Repo Name License Topic
astropy/astropy BSD-3-Clause Scientific/Engineering::Astronomy
django/django BSD-3-Clause Internet::WWW/HTTP
matplotlib/matplotlib Other Scientific/Engineering::Visualization
mwaskom/seaborn BSD-3-Clause Scientific/Engineering::Visualization
pallets/flask BSD-3-Clause Internet::WWW/HTTP
pvlib/pvlib-python BSD-3-Clause Scientific/Engineering::Physics
pydata/xarray Apache-2.0 Scientific/Engineering::Information Analysis
pydicom/pydicom Others Scientific/Engineering::Medical Science Apps.
pylint-dev/astroid LGPL-2.1 Software Development::Libraries
pylint-dev/pylint GPL-2.0 Software Development::Quality Assurance
pyvista/pyvista MIT Scientific/Engineering::Information Analysis
scikit-learn/scikit-learn BSD-3-Clause Scientific/Engineering::Artificial Intelligence
sphinx-doc/sphinx BSD-2-Clause Text Processing::Markup
sqlfluff/sqlfluff MIT Software Development::Quality Assurance
sympy/sympy Others Scientific/Engineering::Mathematics
Aider-AI/aider Apache-2.0 Software Development::Code Generators
Cog-Creators/Red-DiscordBot GPL-3.0 Communications::Chat
DLR-RM/stable-baselines3 MIT Scientific/Engineering::Artificial Intelligence
EleutherAI/lm-evaluation-harness MIT Scientific/Engineering::Artificial Intelligence
Project-MONAI/MONAI Apache-2.0 Scientific/Engineering::Medical Science Apps.
PyThaiNLP/pythainlp Apache-2.0 Text Processing::Linguistic
RDFLib/rdflib BSD-3-Clause Software Development::Libraries
Textualize/rich MIT Software Development::Libraries
Textualize/textual MIT Software Development::User Interfaces
TileDB-Inc/TileDB-Py MIT Software Development::Libraries
astronomer/astronomer-cosmos Apache-2.0 Software Development::Build Tools
atlassian-api/atlassian-python-api Apache-2.0 Internet::WWW/HTTP
aws-cloudformation/cfn-lint MIT-0 Software Development::Quality Assurance
aws-powertools/powertools-lambda-python MIT-0 Software Development::Libraries
aws/sagemaker-python-sdk Apache-2.0 Scientific/Engineering::Artificial Intelligence
biopragmatics/bioregistry MIT Scientific/Engineering::Bio-Informatics
boto/boto3 Apache-2.0 Software Development::Libraries
boto/botocore Apache-2.0 Software Development::Libraries
cocotb/cocotb BSD-3-Clause Scientific/Engineering::Electronic Design Automation (EDA)
conan-io/conan MIT Software Development::Build Tools
deepset-ai/haystack Apache-2.0 Scientific/Engineering::Artificial Intelligence
docker/docker-py Apache-2.0 Software Development::Libraries
dpkp/kafka-python Apache-2.0 Software Development::Libraries
embeddings-benchmark/mteb Apache-2.0 Scientific/Engineering::Artificial Intelligence
facebookresearch/hydra MIT Software Development::Libraries
fairlearn/fairlearn MIT Scientific/Engineering::Artificial Intelligence
falconry/falcon Apache-2.0 Internet::WWW/HTTP
google-deepmind/optax Apache-2.0 Scientific/Engineering::Artificial Intelligence
googleapis/python-aiplatform Apache-2.0 Scientific/Engineering::Artificial Intelligence
googleapis/python-bigquery Apache-2.0 Internet::WWW/HTTP
gradio-app/gradio Apache-2.0 Scientific/Engineering::Human Machine Interfaces
graphql-python/graphene MIT Software Development::Libraries
huggingface/accelerate Apache-2.0 Scientific/Engineering::Artificial Intelligence
huggingface/datasets Apache-2.0 Scientific/Engineering::Artificial Intelligence
huggingface/huggingface_hub Apache-2.0 Scientific/Engineering::Artificial Intelligence
huggingface/pytorch-image-models Apache-2.0 Software Development::Libraries
huggingface/trl Apache-2.0 Scientific/Engineering::Artificial Intelligence
joblib/joblib BSD-3-Clause Software Development::Libraries
joke2k/faker MIT Software Development::Testing
lark-parser/lark MIT Text Processing::Linguistic
minio/minio-py Apache-2.0 Software Development::Libraries
open-mmlab/mmengine Apache-2.0 Utilities
openvinotoolkit/datumaro MIT Scientific/Engineering::Image Processing
pgmpy/pgmpy MIT Scientific/Engineering::Artificial Intelligence
pre-commit/pre-commit MIT Software Development::Quality Assurance
prometheus/client_python Apache-2.0 System::Monitoring
prompt-toolkit/python-prompt-toolkit BSD-3-Clause Software Development::User Interfaces
pygments/pygments BSD-2-Clause Software Development::Documentation
pyocd/pyOCD Apache-2.0 Software Development::Debuggers
pypa/hatch MIT Software Development::Build Tools
pyro-ppl/pyro Apache-2.0 Scientific/Engineering::Artificial Intelligence
python-hyper/h2 MIT Internet::WWW/HTTP
roboflow/supervision MIT Scientific/Engineering::Image Processing
rytilahti/python-miio GPL-3.0 Home Automation
saleweaver/python-amazon-sp-api MIT Internet::WWW/HTTP
scrapy/scrapy BSD-3-Clause Software Development::Libraries
scverse/scanpy BSD-3-Clause Scientific/Engineering::Bio-Informatics
slackapi/bolt-python MIT Communications::Chat
slackapi/python-slack-sdk MIT Communications::Chat
snowflakedb/snowflake-connector-python Apache-2.0 Software Development::Libraries
softlayer/softlayer-python MIT Software Development::Libraries
spec-first/connexion Apache-2.0 Internet::WWW/HTTP
statsmodels/statsmodels BSD-3-Clause Scientific/Engineering::Information Analysis
tfranzel/drf-spectacular BSD-3-Clause Software Development::Documentation
tobymao/sqlglot MIT Database::Database Engines/Servers
tornadoweb/tornado Apache-2.0 Internet::WWW/HTTP
tortoise/tortoise-orm Apache-2.0 Database::Front-Ends
wagtail/wagtail BSD-3-Clause Internet::WWW/HTTP
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