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--- |
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language: |
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- en |
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license: mit |
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size_categories: |
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- n<100 |
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task_categories: |
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- text-generation |
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dataset_info: |
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features: |
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- name: instance_id |
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dtype: string |
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- name: patch |
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dtype: string |
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- name: test_patch |
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dtype: string |
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- name: FAIL_TO_PASS |
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list: string |
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- name: PASS_TO_PASS |
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list: string |
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- name: image_name |
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dtype: string |
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- name: repo |
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dtype: string |
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- name: base_commit |
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dtype: string |
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- name: problem_statement |
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dtype: string |
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- name: repo_settings |
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dtype: string |
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splits: |
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- name: level1 |
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num_bytes: 68545 |
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num_examples: 1 |
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- name: level2 |
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num_bytes: 60238 |
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num_examples: 1 |
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download_size: 60937 |
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dataset_size: 128783 |
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configs: |
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- config_name: default |
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data_files: |
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- split: level1 |
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path: data/level1-* |
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- split: level2 |
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path: data/level2-* |
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tags: |
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- code |
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- agents |
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- software-engineering |
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--- |
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# ACE-Bench: Agent Coding Evaluation Benchmark |
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## Dataset Description |
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ACE-Bench is a comprehensive benchmark designed to evaluate AI agents' capabilities in end-to-end feature-level code generation. Unlike traditional benchmarks that focus on function-level or algorithm-specific tasks, ACE-Bench challenges agents to implement complete features within real-world software projects. |
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### Key Characteristics |
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- **Feature-Level Tasks**: Each task requires implementing a complete feature, including multiple functions, classes, and their interactions |
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- **Real-World Codebases**: Tasks are derived from actual open-source projects, preserving the complexity and context of production code |
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- **End-to-End Evaluation**: Agents must understand requirements, generate code, and pass comprehensive test suites |
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- **Two Difficulty Levels**: |
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- **Level 1**: Agents receive masked code with interface signatures and must implement the complete functionality |
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- **Level 2**: Agents receive only test files and must implement both the interface and functionality from scratch |
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### Dataset Statistics |
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- **Total Instances**: 2 |
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- **Level 1 Instances**: 1 |
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- **Level 2 Instances**: 1 |
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- **Total Size**: 125.76 KB |
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- **Download Size**: 59.51 KB |
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## Dataset Structure |
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Each instance in ACE-Bench contains: |
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- `instance_id`: Unique identifier for the task |
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- `patch`: Git diff showing the implementation (Level 1) or empty string (Level 2) |
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- `test_patch`: Git diff showing test file modifications |
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- `FAIL_TO_PASS`: List of test files that must pass after implementation |
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- `PASS_TO_PASS`: List of test files that must continue passing (Level 1 only) |
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- `image_name`: Docker image containing the development environment |
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- `repo`: Source repository (e.g., "owner/repo-name") |
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- `base_commit`: Git commit hash of the base version |
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- `problem_statement`: Detailed task description and requirements |
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- `repo_settings`: Repository configuration settings as JSON string (from python.py) |
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## Usage |
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```python |
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import json |
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from datasets import load_dataset |
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# Load Level 1 tasks |
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dataset_lv1 = load_dataset("BamChil/ACE-Bench", split="level1") |
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# Load Level 2 tasks |
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dataset_lv2 = load_dataset("BamChil/ACE-Bench", split="level2") |
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# Example: Access a task |
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task = dataset_lv1[0] |
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print(task['instance_id']) |
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print(task['problem_statement']) |
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# Parse repo_settings from JSON string |
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repo_settings = json.loads(task['repo_settings']) |
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print(repo_settings['repository']) |
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print(repo_settings['base_image']) |
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``` |
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