README.md

---
language:
- en
task_categories:
- text-generation
tags:
- code-reasoning
- benchmark
- python
- c
- java
- software-engineering
- llm-evaluation
license: unknown
---

# CodeSense: A Real-World Benchmark and Dataset for Code Semantic Reasoning

This repository contains the dataset and resources for **CodeSense**, the first benchmark for evaluating Large Language Models (LLMs) on fine-grained code semantic reasoning tasks in real-world software engineering contexts. The benchmark was presented in the paper [CodeSense: a Real-World Benchmark and Dataset for Code Semantic Reasoning](https://huggingface.co/papers/2506.00750).

CodeSense aims to bridge the gap between existing synthetic or educational coding problems and the practical demands of software engineering. It utilizes Python, C, and Java software projects from real-world repositories, collecting execution traces to construct a ground truth dataset for detailed semantic reasoning tasks.

**Paper:** [https://huggingface.co/papers/2506.00750](https://huggingface.co/papers/2506.00750)
**Project Page:** [https://codesense-bench.github.io/](https://codesense-bench.github.io/)
**Code Repository:** [https://github.com/codesense-bench/codesense-codes](https://github.com/codesense-bench/codesense-codes)

## Codebase Overview
The associated code repository ([codesense-bench/codesense-codes](https://github.com/codesense-bench/codesense-codes)) contains three main components related to execution tracing, benchmark dataset creation, and LLM evaluation:

### Benchmark Collection
- **Purpose:** Contains scripts to process and clean raw execution traces.
- **Description:** Converts raw traces into task-specific datasets suitable for various code understanding and reasoning benchmarks.

### Tracing Framework
- **Purpose:** Tools for collecting execution traces.
- **Description:** Supports tracing of Python, C, and Java programs to capture their runtime behavior and execution steps.

### LLM Evaluation
- **Purpose:** Scripts for evaluating Large Language Models (LLMs) on the task-specific datasets.
- **Description:** Runs evaluations, computes metrics, and benchmarks model performance on the curated datasets.