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--- |
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language: |
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- en |
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task_categories: |
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- text-generation |
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tags: |
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- code-reasoning |
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- benchmark |
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- python |
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- c |
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- java |
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- software-engineering |
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- llm-evaluation |
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license: unknown |
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--- |
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# CodeSense: A Real-World Benchmark and Dataset for Code Semantic Reasoning |
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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). |
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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. |
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**Paper:** [https://huggingface.co/papers/2506.00750](https://huggingface.co/papers/2506.00750) |
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**Project Page:** [https://codesense-bench.github.io/](https://codesense-bench.github.io/) |
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**Code Repository:** [https://github.com/codesense-bench/codesense-codes](https://github.com/codesense-bench/codesense-codes) |
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## Codebase Overview |
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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: |
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### Benchmark Collection |
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- **Purpose:** Contains scripts to process and clean raw execution traces. |
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- **Description:** Converts raw traces into task-specific datasets suitable for various code understanding and reasoning benchmarks. |
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### Tracing Framework |
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- **Purpose:** Tools for collecting execution traces. |
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- **Description:** Supports tracing of Python, C, and Java programs to capture their runtime behavior and execution steps. |
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### LLM Evaluation |
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- **Purpose:** Scripts for evaluating Large Language Models (LLMs) on the task-specific datasets. |
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- **Description:** Runs evaluations, computes metrics, and benchmarks model performance on the curated datasets. |
