Datasets:
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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:
```json id="m4kqv2"
{
"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 |