自动识别生成代码的来源和许可信息，提升代码可信度。
CodeGenLink: A Tool to Find the Likely Origin and License of Automatically Generated Code
- 结合大模型与网络搜索，检索相似代码链接。
- 通过相似度分析过滤无关结果，准确率超80%。
- 适合关注代码版权合规性的开发者使用。
大型语言模型（LLMs）如今广泛应用于软件开发任务中。与从网络直接复用代码不同，由LLM生成的代码因缺乏来源信息，开发者对其可信度及潜在版权或许可证违规问题感到担忧。本文提出CodeGenLink，一个专为Visual Studio Code设计的GitHub CoPilot扩展工具，旨在（i）建议与自动生成代码高度相似的代码链接，（ii）在可能情况下，标注代码可能来源的许可证信息。CodeGenLink通过结合大模型的搜索能力与代码相似性分析，从网络检索候选链接并进行比对。初步实验表明，该工具能有效过滤无关链接，且在多数情况下可提供许可证信息。工具地址：https://github.com/danielebifolco/CodeGenLink，演示视频：https://youtu.be/M6nqjBf9_pw。
原文摘要 · Abstract (English)
Large Language Models (LLMs) are widely used in software development tasks nowadays. Unlike reusing code taken from the Web, for LLMs' generated code, developers are concerned about its lack of trustworthiness and possible copyright or licensing violations, due to the lack of code provenance information. This paper proposes CodeGenLink, a GitHub CoPilot extension for Visual Studio Code aimed at (i) suggesting links containing code very similar to automatically generated code, and (ii) whenever possible, indicating the license of the likely origin of the code. CodeGenLink retrieves candidate links by combining LLMs with their web search features and then performs similarity analysis between the generated and retrieved code. Preliminary results show that CodeGenLink effectively filters unrelated links via similarity analysis and provides licensing information when available. Tool URL: https://github.com/danielebifolco/CodeGenLink Tool Video: https://youtu.be/M6nqjBf9_pw
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