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---
license: cc-by-sa-4.0
tags:
- competitive-programming
- code-ranking
- llm-benchmark
- code-efficiency
- aizu-online-judge
---
# AOJ-CodeRank-Benchmark: Hybrid Efficiency Ranking Benchmark Dataset
## 1. Overview
This dataset (AOJ-CodeRank-Benchmark) was created to evaluate the capability of **Large Language Models (LLMs)** in **code efficiency ranking tasks** using a high-quality, structured benchmark.
The dataset is built entirely on code submission records from **Aizu Online Judge (AOJ)**, strictly adhering to the principle of **correctness first, efficiency second**.
* **Problem Scope**: ALDS1 (Fundamental Algorithms), DSL/GRL/CGL (Advanced Data Structures/Graphs), and Volume 0000-3299 (Classic Contest Problems).
* **Core Feature**: **Eliminates** 0ms submissions and low-quality/non-unique submissions, ensuring true time differentiation across all data groups.
## 2. Data Structure
The dataset uses the **JSON Lines (.jsonl)** format. Each line represents a single Task Group object.
**Structure Preview (Candidates):**
| Field Name | Type | Description |
| :--- | :--- | :--- |
| `submission_id` | string | Unique Submission ID. |
| `code_snippet` | string | The complete C++ source code. |
| **`accuracy`** | float | **Accuracy Score** (0.0 to 1.0). |
| `time_ms` | integer | Actual Execution Time (in milliseconds). |
| **`score_of_the_acc`** | float | **Normalized Efficiency Score** (Range -2.0 to 0.0). |
| **`final_rank`** | integer | **Final Competition Rank** (1, 2, 3...). |
## 3. Ground Truth (GT) Scoring and Ranking Logic 🏆
The LLM's objective is to predict the `final_rank`. This ranking is derived from a unique two-tiered system:
### Phase I: Efficiency Score (`score_of_the_acc`)
This score is a purely performance-based metric, calculating the normalized inverse sum of Time and Memory costs within the task group.
$$ ext{Score} = -( ext{Norm\_Time} + ext{Norm\_Memory})$$
*(Note: Score is between -2.0 and 0.0. A score closer to 0.0 is better.)*
### Phase II: Final Ranking (`final_rank`) Mechanism
The final rank is determined by a lexicographical sort (Standard Competition Ranking) using the following priority:
1. **Primary Sort Key (Accuracy)**: **`accuracy`** (Descending).
2. **Secondary Sort Key (Efficiency)**: **`score_of_the_acc`** (Descending).
**Tie-Breaking**: Submissions with identical Accuracy and Efficiency Score receive the same rank (1-2-2-4 rule).
---
### 4. Usage Example
```python
from datasets import load_dataset
# Load the dataset and access the candidates list
dataset = load_dataset("Slime/AOJ-CodeRank-Benchmark", data_files="train.jsonl", split="train")
# The LLM sorting algorithm will receive task['candidates'] for ranking
for task in dataset:
candidates = task['candidates']
# Algorithm generates predicted_rank for candidates
# Evaluation compares predicted_rank against ground_truth['final_rank']
```
### 5. Acknowledgments
Original submission records and problem context are sourced from Aizu Online Judge (AOJ). |