Datasets:
File size: 4,264 Bytes
01c42d2 93f6641 ced758a 6d2437b ced758a e61a1bb ced758a fd6fd35 93f6641 fd6fd35 ced758a 69b8c03 93f6641 76e1f1e ced758a 01c42d2 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 |
---
license: apache-2.0
task_categories:
- question-answering
language:
- en
tags:
- LLM4code
- code_reasoning
- neurips25
size_categories:
- 10K<n<100K
---
# CoRe: Benchmarking LLMs’ Code Reasoning Capabilities through Static Analysis Tasks
This repository hosts the **CoRe** benchmark, designed to evaluate the reasoning capabilities of large language models on **program analysis tasks** including data dependency, control dependency, and information flow. Each task instance is represented as a structured JSON object with detailed metadata for evaluation and reproduction.
It contains 25k data points (last update: Sep. 24th, 2025).
Each example is a JSON object with the following fields:
```json
{
"label_file": "codenet_p00496_s700056700_main_12_40.yaml",
"code_file": "codenet_p00496_s700056700_main_12_40.c",
"pid": "p00496",
"sid": "s700056700",
"funname": "main",
"start": 12,
"end": 40,
"dataset": "codenet",
"language": "C",
"src": 30,
"dst": 33,
"groundtruth": true,
"task_id": "control_codenet_p00496_s700056700_main_12_40_k_33_1",
"prompt": "..."
"category": trace/all_source
}
```
### 🏷 Category Field
The `category` field specifies the type of prompt associated with each task instance:
* **trace**: The prompt asks the model to produce a dependency trace if the answer is `yes` (e.g., the control or data dependency exists).
* **all\_source**: The prompt asks the model to enumerate all source elements involved in the dependency.
## 🧩 Field Descriptions
| Field | Description |
|------------------|-------------|
| `label_file` | Path to the YAML file containing ground truth annotations for the current task instance. |
| `code_file` | Path to the corresponding C/Java/Python source code file. |
| `pid` | Problem ID from the original source dataset (e.g., CodeNet or GCJ). |
| `sid` | Solution ID identifying the specific program implementation. |
| `funname` | Name of the target function in which the analysis is conducted. |
| `start`, `end` | Line numbers defining the start and end of the target function. |
| `dataset` | Original dataset source (`codenet` or `gcj`). |
| `language` | Programming language of the source file (`C`, `Java`, `Python`). |
| `src`, `dst` | Defines the two program elements queried in this task. In control dependency, these are line numbers. In data dependency and information flow, they are structured as `["varname", line_no]`, representing variable instances. |
| `groundtruth` | Boolean indicating whether the specified dependency relationship holds (i.e., true if `src` has the given dependency on `dst`). |
| `task_id` | A unique ID for the task instance. The prefix (`control_`, `data_`, `infoflow_`) identifies the task type. |
| `prompt` | The prompt string used in the experiment for this task instance. It includes the instruction, examples, query, and code context provided to the LLM. Content-specific fields (e.g., source/target names, line numbers) are filled into a standardized prompt template. |
## 📚 Task Types
The benchmark contains three types of program reasoning tasks:
- `control`: Control dependency between lines.
- `data`: Data dependency between variables.
- `infoflow`: Information flow (explicit or implicit) between variables.
Each instance is designed to assess whether an LLM can understand and reason over static semantics in real-world source code.
## 🛠 Scripts and Usage
For scripts, evaluation tools, and detailed instructions on running inference over CoRe, please check out our companion GitHub repository:
🔗 Website: [https://corebench.github.io/](https://corebench.github.io/)
🔗 Source code: [https://github.com/CoReBench/CoRe](https://github.com/CoReBench/CoRe)
🔗 Paper: [https://arxiv.org/abs/2507.05269](https://arxiv.org/abs/2507.05269)
The github repo includes:
- Raw annotation data that could be used to generate various static analysis tasks
- Predefined prompts for each task and language
- Scripts for invoking models and parsing responses
- Evaluation scripts for dependency classification, trace generation, and dependency source enumeration
### 📄 License
Apache License 2.0 |