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Authorship attribution (i.e., determining who is the author of a piece ofsource code) is an established research topic. State-of-the-art results for theauthorship attribution problem look promising for the software engineeringfield, where they could be applied to detect plagiarized code and prevent legalissues. With this article, we first introduce a new language-agnostic approachto authorship attribution of source code. Then, we discuss limitations ofexisting synthetic datasets for authorship attribution, and propose a datacollection approach that delivers datasets that better reflect aspectsimportant for potential practical use in software engineering. Finally, wedemonstrate that high accuracy of authorship attribution models on existingdatasets drastically drops when they are evaluated on more realistic data. Weoutline next steps for the design and evaluation of authorship attributionmodels that could bring the research efforts closer to practical use forsoftware engineering.

Authorship Attribution of Source Code: A Language-Agnostic Approach and Applicability in Software Engineering