  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.
