Datasets:

BamChil
/

ACE-Bench

language:
  - en
license: mit
size_categories:
  - n<100
task_categories:
  - text-generation
dataset_info:
  features:
    - name: instance_id
      dtype: string
    - name: patch
      dtype: string
    - name: test_patch
      dtype: string
    - name: FAIL_TO_PASS
      list: string
    - name: PASS_TO_PASS
      list: string
    - name: image_name
      dtype: string
    - name: repo
      dtype: string
    - name: base_commit
      dtype: string
    - name: problem_statement
      dtype: string
    - name: repo_settings
      dtype: string
  splits:
    - name: level1
      num_bytes: 68545
      num_examples: 1
    - name: level2
      num_bytes: 60238
      num_examples: 1
  download_size: 60937
  dataset_size: 128783
configs:
  - config_name: default
    data_files:
      - split: level1
        path: data/level1-*
      - split: level2
        path: data/level2-*
tags:
  - code
  - agents
  - software-engineering

ACE-Bench: Agent Coding Evaluation Benchmark

Dataset Description

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.

Key Characteristics

Feature-Level Tasks: Each task requires implementing a complete feature, including multiple functions, classes, and their interactions
Real-World Codebases: Tasks are derived from actual open-source projects, preserving the complexity and context of production code
End-to-End Evaluation: Agents must understand requirements, generate code, and pass comprehensive test suites
Two Difficulty Levels:
- Level 1: Agents receive masked code with interface signatures and must implement the complete functionality
- Level 2: Agents receive only test files and must implement both the interface and functionality from scratch

Dataset Statistics

Total Instances: 2
Level 1 Instances: 1
Level 2 Instances: 1
Total Size: 125.76 KB
Download Size: 59.51 KB

Dataset Structure

Each instance in ACE-Bench contains:

instance_id: Unique identifier for the task
patch: Git diff showing the implementation (Level 1) or empty string (Level 2)
test_patch: Git diff showing test file modifications
FAIL_TO_PASS: List of test files that must pass after implementation
PASS_TO_PASS: List of test files that must continue passing (Level 1 only)
image_name: Docker image containing the development environment
repo: Source repository (e.g., "owner/repo-name")
base_commit: Git commit hash of the base version
problem_statement: Detailed task description and requirements
repo_settings: Repository configuration settings as JSON string (from python.py)

Usage

import json
from datasets import load_dataset

# Load Level 1 tasks
dataset_lv1 = load_dataset("BamChil/ACE-Bench", split="level1")

# Load Level 2 tasks
dataset_lv2 = load_dataset("BamChil/ACE-Bench", split="level2")

# Example: Access a task
task = dataset_lv1[0]
print(task['instance_id'])
print(task['problem_statement'])

# Parse repo_settings from JSON string
repo_settings = json.loads(task['repo_settings'])
print(repo_settings['repository'])
print(repo_settings['base_image'])