Add initial dataset card for CodeSense

README.md

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+---
+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.