datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
lca-bug-localization
🏟️ Long Code Arena (Bug localization)
This is the benchmark for the Bug localization task as part of the
🏟️ Long Code Arena benchmark.
The bug localization problem can be formulated as follows: given an issue with a bug description and a repository snapshot in a state where the bug is reproducible, identify the files within the repository that need to be modified to address the reported bug.
The dataset provides all the required components for evaluation of bug localization… See the full description on the dataset page: https://huggingface.co/datasets/JetBrains-Research/lca-bug-localization.hf-bug-tempbug-localization
Bug Localization
This is the data for Bug Localization benchmark.
How-to
Since the dataset is private, if you haven't used HF Hub before, add your token via huggingface-cli first:
huggingface-cli login
List all the available configs via datasets.get_dataset_config_names and choose an appropriate one
Load the data via load_dataset:
from datasets import load_dataset
# Select a configuration from ["py", "java", "kt", "mixed"]
configuration = "py"
# Select a split from… See the full description on the dataset page: https://huggingface.co/datasets/tiginamaria/bug-localization.bugbounty-hunter-v1
Bug Bounty Hunter v1 - Fine-Tuning Dataset
The largest public dataset for training AI models on bug bounty hunting expertise.
Overview
52,743 instruction-response pairs covering 40+ vulnerability types across 6 categories, extracted from real-world bug bounty writeups and security research.
Dataset Summary
This dataset is designed to fine-tune language models to become expert bug bounty hunters. Each example contains a technical instruction and a… See the full description on the dataset page: https://huggingface.co/datasets/0xn0cta/bugbounty-hunter-v1.rl_rl-conf_24GP_base-yaml_mode-path_r2eg-nl2b-stac-bugs-fixt_trai-data_exp_rpt_stac-php-largierd-codeforces-subtle-bugs
IERD Codeforces subtle bugs
This public dataset contains 682 generated buggy C++ solutions for 682 Codeforces
problems. Each solution passes most tests in the frozen source corpus and fails from
one to five stored human or Hugging Face tests. The package also contains the frozen
manifest, provenance files, and aggregate reports from the final test generation
study.
Source and version
The problems, tests, and reference solution candidates come from… See the full description on the dataset page: https://huggingface.co/datasets/shivank21/ierd-codeforces-subtle-bugs.SWEUniverse-Repaired-Bugpilot
SWEUniverse Repaired Bugpilot
Reformatted repaired dataset from VmaxRL/SWEUniverse-Repaired-Bugpilot.
Rows: 804
Columns are exactly:
Task id
initial p2p
new p2p
initial f2p
new f2p
default test command
new test command
test parser
image name
patch
problem statement
repo
initial f2p/initial p2p are the source labels before repair; new f2p/new p2p are the repaired labels used for evaluation.
SWE-bench_mini_bug_input_check_bug_report__fs-oracleSWE-bench_validated_12_18_bug_report_bug_report__fs-oracleBuggedPythonLeetCode
Dataset Description
edit: fixed some bugs with datasets not handling all pyarrow types.
Dataset Summary
This dataset consists of Python coding problems from LeetCode, which have been bugged using the OpenBugger package. This dataset provides a unique opportunity to study the debugging process in a controlled and replicable environment.
For each correct code snippet, 15 bugged versions were attempted. For each succesfully bugged version, a corresponding question mimicking… See the full description on the dataset page: https://huggingface.co/datasets/NeuroDragon/BuggedPythonLeetCode.SWE-bench_4o_bug_input_check_bug_report__fs-oracleSWE-bench_asterinas_bug_report__fs-oraclerl_rl-conf_24GP_base-yaml_mode-path_r2eg-nl2b-stac-bugs-fixt_trai-data_exp_rpt_stac-self-largbenchname-bug-localization
🥷 BenchName (Bug localization)
This is the benchmark for the Bug localization task as part of the
🥷 BenchName benchmark.
The bug localization problem can be formulated as follows: given an issue with a bug description and a repository snapshot in a state where the bug is reproducible, identify the files within the repository that need to be modified to address the reported bug.
The dataset provides all the required components for evaluation of bug localization approaches in… See the full description on the dataset page: https://huggingface.co/datasets/anon-iclr-submission/benchname-bug-localization.rl_rl-conf_24GP_base-yaml_mode-path_r2eg-nl2b-stac-bugs-fixt_trai-data_exp_rpt_pyme-largfantastic-bugs
Fantastic Bugs and Where to Find Them in AI Benchmarks
NeurIPS 2025
This dataset accompanies the paper Fantastic Bugs and Where to Find Them in AI Benchmarks. It provides both the raw data for running the analysis pipeline and the curated output of detected anomalous items.
Repository Structure
fantastic-bugs/
├── data/ # Curated output (404 anomalous items with expert reviews)
│ ├── gsm-*.parquet
│ ├── med_qa-*.parquet
│ └── ...
│
└── raw/… See the full description on the dataset page: https://huggingface.co/datasets/stair-lab/fantastic-bugs.rl_rl-conf_24GP_base-yaml_mode-path_r2eg-nl2b-stac-bugs-fixt-agai_trai-data_exp_rpt_stac-rustSWEUniverse-Repaired-Bugpilot-350
SWEUniverse-Repaired-Bugpilot-350
A deterministic 350-task subset in the swe_fix_training_row_v1 format introduced in PR #1942.
Schema: swe_fix_training_row_v1
Source dataset: VmaxRL/SWEUniverse-Repaired-Bugpilot
Rows: 350
Split: train
Task variant: generated_bug_intro
Source kind: swesmith
Generator id: bugpilot
The subset was sampled from validated accepted tasks and preserves the validated repository distribution as closely as possible. pass_to_pass excludes any labels also… See the full description on the dataset page: https://huggingface.co/datasets/VmaxRL/SWEUniverse-Repaired-Bugpilot-350.red-pyjama-sample-1T-max-chunk-16krl_rl-conf_24GP_base_noth-yaml_mode-path_r2eg-nl2b-stac-bugs_trai-data_exp_rpt_stac-self-larglcb_bugbenchbuggy-appsThis dataset was constructed for use in the paper Neural Interactive Proofs. It is based on the APPS benchmark for code generation (see also the corresponding Hugging Face dataset). It includes includes a number of coding problems with both buggy and non-buggy solutions (though note that, apparently, in AlphaCode the authors found that this dataset can generate many false positives during evaluation, where incorrect submissions are marked as correct due to lack of test coverage).
Each datum… See the full description on the dataset page: https://huggingface.co/datasets/lrhammond/buggy-apps.bugbounty-hunter-v1
Bug Bounty Hunter v1 - Fine-Tuning Dataset
The largest public dataset for training AI models on bug bounty hunting expertise.
Overview
52,743 instruction-response pairs covering 40+ vulnerability types across 6 categories, extracted from real-world bug bounty writeups and security research.
Dataset Summary
This dataset is designed to fine-tune language models to become expert bug bounty hunters. Each example contains a technical instruction and a… See the full description on the dataset page: https://huggingface.co/datasets/ArkhAngelLifeJiggy/bugbounty-hunter-v1.linux-kernel-bugfixes-diffs
🐧 Linux Kernel Bugfixes & Patches Dataset (Instruction-Tuned)
📖 Dataset Description
This dataset is a highly curated, instruction-tuned collection of problem-solution pairs extracted directly from the official Linux Kernel Git repository (torvalds/linux). It is specifically designed to train Large Language Models (LLMs) on low-level C programming, kernel architecture, memory management, and security vulnerability patching.
Unlike raw commit histories, this… See the full description on the dataset page: https://huggingface.co/datasets/switlydev/linux-kernel-bugfixes-diffs.rl_rl-conf_24GP_base_noth-yaml_mode-path_r2eg-nl2b-stac-bugs_trai-data_exp_rpt_pyme-larginput_tools_plans
Dataset Card for "input_tools_plans"
More Information needed
BeetleBox
Dataset Card for Dataset Name
BeetleBox
Dataset Details
The BeetleBox dataset is a comprehensive multi-language, multi-project dataset designed for bug localization research. It includes 26,321 bugs from 29 projects, covering five major programming languages: Java, Python, C++, JavaScript, and Go. The dataset was meticulously curated to ensure accuracy, with a manual analysis revealing an incorrect ground truth rate of only 0.06%.
Dataset Description
The… See the full description on the dataset page: https://huggingface.co/datasets/bug-localization/BeetleBox.auto_0207_bugbugs_human_edited_lm_evalBugsInPy-logs