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--- |
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license: apache-2.0 |
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task_categories: |
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- question-answering |
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language: |
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- en |
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tags: |
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- LLM4code |
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- code_reasoning |
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- neurips25 |
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size_categories: |
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- 10K<n<100K |
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--- |
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# CoRe: Benchmarking LLMs’ Code Reasoning Capabilities through Static Analysis Tasks |
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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. |
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It contains 25k data points (last update: Sep. 24th, 2025). |
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Each example is a JSON object with the following fields: |
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```json |
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{ |
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"label_file": "codenet_p00496_s700056700_main_12_40.yaml", |
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"code_file": "codenet_p00496_s700056700_main_12_40.c", |
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"pid": "p00496", |
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"sid": "s700056700", |
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"funname": "main", |
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"start": 12, |
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"end": 40, |
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"dataset": "codenet", |
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"language": "C", |
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"src": 30, |
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"dst": 33, |
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"groundtruth": true, |
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"task_id": "control_codenet_p00496_s700056700_main_12_40_k_33_1", |
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"prompt": "..." |
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"category": trace/all_source |
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} |
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``` |
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### 🏷 Category Field |
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The `category` field specifies the type of prompt associated with each task instance: |
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* **trace**: The prompt asks the model to produce a dependency trace if the answer is `yes` (e.g., the control or data dependency exists). |
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* **all\_source**: The prompt asks the model to enumerate all source elements involved in the dependency. |
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## 🧩 Field Descriptions |
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| Field | Description | |
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|------------------|-------------| |
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| `label_file` | Path to the YAML file containing ground truth annotations for the current task instance. | |
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| `code_file` | Path to the corresponding C/Java/Python source code file. | |
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| `pid` | Problem ID from the original source dataset (e.g., CodeNet or GCJ). | |
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| `sid` | Solution ID identifying the specific program implementation. | |
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| `funname` | Name of the target function in which the analysis is conducted. | |
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| `start`, `end` | Line numbers defining the start and end of the target function. | |
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| `dataset` | Original dataset source (`codenet` or `gcj`). | |
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| `language` | Programming language of the source file (`C`, `Java`, `Python`). | |
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| `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. | |
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| `groundtruth` | Boolean indicating whether the specified dependency relationship holds (i.e., true if `src` has the given dependency on `dst`). | |
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| `task_id` | A unique ID for the task instance. The prefix (`control_`, `data_`, `infoflow_`) identifies the task type. | |
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| `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. | |
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## 📚 Task Types |
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The benchmark contains three types of program reasoning tasks: |
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- `control`: Control dependency between lines. |
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- `data`: Data dependency between variables. |
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- `infoflow`: Information flow (explicit or implicit) between variables. |
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Each instance is designed to assess whether an LLM can understand and reason over static semantics in real-world source code. |
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## 🛠 Scripts and Usage |
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For scripts, evaluation tools, and detailed instructions on running inference over CoRe, please check out our companion GitHub repository: |
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🔗 Website: [https://corebench.github.io/](https://corebench.github.io/) |
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🔗 Source code: [https://github.com/CoReBench/CoRe](https://github.com/CoReBench/CoRe) |
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🔗 Paper: [https://arxiv.org/abs/2507.05269](https://arxiv.org/abs/2507.05269) |
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The github repo includes: |
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- Raw annotation data that could be used to generate various static analysis tasks |
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- Predefined prompts for each task and language |
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- Scripts for invoking models and parsing responses |
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- Evaluation scripts for dependency classification, trace generation, and dependency source enumeration |
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### 📄 License |
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Apache License 2.0 |
