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

arXiv: https://arxiv.org/abs/2001.11593 · HTML: https://mlsanity.com/paper/2001.11593/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Jan 2020.

## Built on by (most cited first, 9 of 9)

- [Malla: Demystifying Real-world Large Language Model Integrated Malicious Services](https://mlsanity.com/paper/2401.03315/) (2024)
- [RoPGen: Towards Robust Code Authorship Attribution via Automatic Coding Style Transformation](https://mlsanity.com/paper/2202.06043/) (2022)
- [Abstract Syntax Tree for Programming Language Understanding and Representation: How Far Are We?](https://mlsanity.com/paper/2312.00413/) (2023)
- [The Hidden DNA of LLM-Generated JavaScript: Structural Patterns Enable High-Accuracy Authorship Attribution](https://mlsanity.com/paper/2510.10493/) (2025)
- [ICodeNet -- A Hierarchical Neural Network Approach for Source Code Author Identification](https://mlsanity.com/paper/2102.00230/) (2021)
- [I still know it's you! On Challenges in Anonymizing Source Code](https://mlsanity.com/paper/2208.12553/) (2022)
- [GoCoMA: Hyperbolic Multimodal Representation Fusion for Large Language Model-Generated Code Attribution](https://mlsanity.com/paper/2604.16377/) (2026)
- [AgenTag: Attribution of AI Coding Agents from Behavioral Fingerprints](https://mlsanity.com/paper/2608.00966/) (2026)
- [Stylometric Defenses Against Author Impersonation in Software Repositories](https://mlsanity.com/paper/2608.02695/) (2026)

## Abstract (arXiv)

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.