Authorship Attribution of Source Code
Reviewed by Greg Wilson / 2021-10-15
Keywords: Authorship, Machine Learning
The question, "Who actually wrote this code?" comes up in many contexts, from plagiarism detection in schoolwork to design recovery in legacy systems. Bogomolov2021 presents two machine learning approaches to the problem using neural networks and random forests. Unlike most earlier work, these models operate on paths through the source code's abstract syntax tree (AST). The authors find that:
- their random forest approach outperforms the previous best result on C++,
- it matches the best performance of previous systems on Python, and
- both of their approaches outperform previous results on Java.
I have reservations about how eagerly and uncritically some researchers are applying machine learning to software engineering problems, but this study seems to have been well designed and well controlled, and their use of ASTs to make their tools language-agnostic is really interesting. I look forward to hearing more from this team.
Bogomolov2021 Egor Bogomolov, Vladimir Kovalenko, Yurii Rebryk, Alberto Bacchelli, and Timofey Bryksin: "Authorship attribution of source code: a language-agnostic approach and applicability in software engineering". Proc. European Software Engineering Conference/International Symposium on the Foundations of Software Engineering (ESEC/FSE), 2021, 10.1145/3468264.3468606.
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.