New Benchmark Dataset Enables Detection of AI-Generated Code in Mixed Human-AI Codebases
Researchers have introduced HybridCodeAuthorship, a benchmark dataset designed to test algorithms that detect AI-generated code at the line level within real-world-style Python files. Unlike prior benchmarks that treat code as entirely human- or AI-authored, this dataset simulates how developers actually use AI coding assistants by interleaving human and AI contributions. The findings reveal that current detection algorithms perform poorly on this more realistic task, raising concerns for software risk management and productivity analysis.
A team of researchers has published HybridCodeAuthorship, a new benchmark dataset accepted to LREC 2026, aimed at evaluating algorithms that identify AI-generated code at a fine-grained, line-by-line level within Python source files. The dataset was constructed using CodeSearchNet, a large collection of open-source GitHub repositories, and is designed to reflect authentic AI coding assistant usage rather than the isolated, academic-style problems common in existing benchmarks. The researchers evaluated two state-of-the-art AI-generated code detection algorithms on both line-level and chunk-level tasks. The best-performing system, AIGCode Detector, achieved F1 scores of only 0.56 at the line level and 0.48 at the chunk level, indicating that current methods fall well short of reliable detection. The study highlights a significant gap between existing benchmarks and the messy, hybrid reality of modern industry codebases, where AI and human contributions are deeply interleaved. This matters because accurately identifying AI-generated code is increasingly important for software auditing, intellectual property considerations, and understanding developer productivity.
What's missing
The paper does not detail the specific LLMs or AI coding assistants used to generate the AI-authored portions of the dataset, which limits understanding of how well results generalize across different AI tools. It is also unclear how the dataset handles code that was AI-generated but subsequently edited by humans, a common real-world scenario. The study focuses exclusively on Python, leaving open questions about generalizability to other programming languages.
What different sources said
- arXiv cs.AICenter
HybridCodeAuthorship: A Benchmark Dataset for Line-Level Code Authorship Detection
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