README.md

metadata

license: mit
task_categories:
  - text-generation
language:
  - zh
  - en
tags:
  - ARP
  - RL
  - verl
  - LLM
pretty_name: DeCoMo
size_categories:
  - 10K<n<100K

---

📁 Dataset Variants

1. preprocess-CodeContests-perturbation-none

• No perturbation applied
• Pure clean programs
• Used as baseline reference

2. preprocess-CodeContests-perturbation-syntax

• Only syntax-level perturbations
• Bug types: token deletion, indentation errors, malformed expressions
• Focus: syntax recovery ability

3. preprocess-CodeContests-perturbation-structure

• Only structure-level perturbations
• Bug types: line swap, line deletion, statement reordering
• Focus: program structure repair

4. preprocess-CodeContests-perturbation-semantic

• Only semantic-level perturbations
• Bug types: logic errors, wrong operators, boundary mistakes
• Focus: semantic reasoning repair

5. preprocess-CodeContests-perturbation-mix

• Mixed perturbation dataset
• Combines syntax + structure + semantic corruption
• Used for general robustness training

6. preprocess-CodeContests-perturbation-mixed-task-*

These are controlled mixture ratio subsets used for ablation and scaling.

Format:

00100 → 0 : 10 : 0 : 0
01000 → 0 : 0 : 10 : 0
10000 → 10 : 0 : 0 : 0
2242  → 2 : 2 : 4 : 2

Where the order is:

[syntax : structure : semantic : mixed]

Examples:

• 00100 → only structure perturbation (100%)
• 01000 → only semantic perturbation (100%)
• 10000 → only syntax perturbation (100%)
• 2242 → balanced mixture (2:2:4:2)

---

📦 Data Format

Each sample contains:

{
  "id": "problem_id",
  "clean_code": "correct reference solution",
  "buggy_code": "perturbed program",
  "perturbation_type": "syntax | structure | semantic | mixed",
  "language": "python",
  "task_prompt": "problem statement (optional)",
  "test_cases": {
    "input": "...",
    "output": "..."
  }
}

🎯 Purpose

These variants are designed for:

• perturbation-driven program repair
• RL-based trajectory learning
• ablation over bug types
• robustness evaluation under different corruption distributions