Source Code的作者归属：一种与文档无关的方法及其在软件工程中的适用性
Authorship Attribution of Source Code: A Language-Agnostic Approach and Applicability in Software Engineering
摘要：
作者归属（即，确定谁是一段源代码的作者）是一个既定的研究主题。作者归属问题的最新结果在软件工程领域很有前途，可以应用于检测剽窃代码和防止法律问题。在本文中，我们首先介绍一种新的与语言无关的源代码作者归属方法。然后，我们讨论了现有的合成数据集的作者归属的局限性，并提出了一种数据汇集方法，提供的数据集，更好地反映在软件工程的潜在的实际用途的重要方面。最后，我们证明了作者归属模型在现有数据集上的高准确性在更真实的数据上进行评估时会急剧下降。我们概述了作者归属模型的设计和评估的下一步，可以使研究工作更接近实际使用的软件工程。
英文摘要：
Authorship attribution (i.e., determining who is the author of a piece of source code) is an established research topic. State-of-the-art results for the authorship attribution problem look promising for the software engineering field, where they could be applied to detect plagiarized code and prevent legal issues. With this article, we first introduce a new language-agnostic approach to authorship attribution of source code. Then, we discuss limitations of existing synthetic datasets for authorship attribution, and propose a data collection approach that delivers datasets that better reflect aspects important for potential practical use in software engineering. Finally, we demonstrate that high accuracy of authorship attribution models on existing datasets drastically drops when they are evaluated on more realistic data. We outline next steps for the design and evaluation of authorship attribution models that could bring the research efforts closer to practical use for software engineering.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
