HybridCodeAuthorship: A Benchmark Dataset for Line-Level Code Authorship Detection
📰 ArXiv cs.AI
Learn to detect AI-generated code with HybridCodeAuthorship, a benchmark dataset for line-level code authorship detection, crucial for risk management and productivity analysis
Action Steps
- Collect and preprocess code datasets using HybridCodeAuthorship
- Train machine learning models to detect AI-generated code
- Evaluate model performance using metrics such as accuracy and F1-score
- Fine-tune models to improve detection accuracy
- Integrate detection models into CI/CD pipelines for automated code analysis
Who Needs to Know This
Software engineers, AI researchers, and DevOps teams can benefit from this benchmark dataset to develop and evaluate algorithms for detecting AI-generated code, improving code quality and security
Key Insight
💡 HybridCodeAuthorship provides a benchmark dataset for line-level code authorship detection, enabling development of algorithms to identify AI-generated code
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🚀 Detect AI-generated code with HybridCodeAuthorship! 🤖💻
Key Takeaways
Learn to detect AI-generated code with HybridCodeAuthorship, a benchmark dataset for line-level code authorship detection, crucial for risk management and productivity analysis
Full Article
Title: HybridCodeAuthorship: A Benchmark Dataset for Line-Level Code Authorship Detection
Abstract:
arXiv:2606.12620v1 Announce Type: cross Abstract: Thanks to the rapid adoption of AI code assistants powered by large language models (LLMs), industry codebases are, increasingly, a hybrid of AI- and human-authored code. For risk management and productivity analysis purposes, it is crucial to enable fine-grained location detection of AI-generated code. To develop algorithms for this task, quality benchmarks are needed to assess performance. However, existing benchmarks tend to comprise academic,
Abstract:
arXiv:2606.12620v1 Announce Type: cross Abstract: Thanks to the rapid adoption of AI code assistants powered by large language models (LLMs), industry codebases are, increasingly, a hybrid of AI- and human-authored code. For risk management and productivity analysis purposes, it is crucial to enable fine-grained location detection of AI-generated code. To develop algorithms for this task, quality benchmarks are needed to assess performance. However, existing benchmarks tend to comprise academic,
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