一句话：提出语言无关的源代码作者归属方法，并揭示现有数据集上的高准确率在更真实数据上大幅下降。
源代码作者归属旨在判断一段代码的作者，在查重和规避法律风险等软件工程场景中具有应用前景，但现有研究多在合成数据集上取得高精度。本文提出一种语言无关的作者归属方法，并指出现有合成数据集在反映实际软件工程需求方面的局限，进而提出新的数据收集方法以构建更贴近真实场景的数据集。评估表明，作者归属模型在原有数据集上的高准确率在更真实数据上会大幅下降。作者据此讨论提升实际可用性的模型设计与评估方向。
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.
@inproceedings{paperbot6463,
title = {Authorship attribution of source code: a language-agnostic approach and applicability in software engineering},
author = {Egor Bogomolov and Vladimir Kovalenko and Yurii Rebryk and Alberto Bacchelli and Timofey Bryksin},
booktitle = {FSE},
year = {2021},
doi = {10.1145/3468264.3468606}
}