Reassessing Code Authorship Attribution in the Era of Language Models

• URL: http://arxiv.org/abs/2506.17120v1
• Date: Fri, 20 Jun 2025 16:19:30 GMT
• Title: Reassessing Code Authorship Attribution in the Era of Language Models
• Authors: Atish Kumar Dipongkor, Ziyu Yao, Kevin Moran,
• Abstract summary: This study aims to analyze coding styles to identify the authors of code samples.<n>Code Authorship Attribution (CAA) is crucial in cybersecurity and software for addressing, detecting plagiarism, and supporting criminal prosecutions.
• Score: 12.590406993068523
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The study of Code Stylometry, and in particular Code Authorship Attribution (CAA), aims to analyze coding styles to identify the authors of code samples. CAA is crucial in cybersecurity and software forensics for addressing, detecting plagiarism, and supporting criminal prosecutions. However, CAA is a complex and error prone task, due to the need for recognizing nuanced relationships between coding patterns. This challenge is compounded in large software systems with numerous authors due to the subtle variability of patterns that signify the coding style of one author among many. Given the challenges related to this task, researchers have proposed and studied automated approaches that rely upon classical Machine Learning and Deep Learning techniques. However, such techniques have historically relied upon hand-crafted features, and due to the often intricate interaction of different features (e.g., formatting, etc.), have key limitations in properly characterizing authorship, and are sensitive to adversarial code perturbations. Recently, transformer-based Language Models (LMs) have shown remarkable efficacy across a range of software engineering tasks, and in the authorship attribution on natural language in the NLP domain. However, their effectiveness in CAA is not well understood. As such, we conduct the first extensive empirical study applying two larger state-of-the-art code LMs, and five smaller code LMs to the task of CAA to 6 diverse datasets that encompass 12k code snippets written by 463 developers. Furthermore, we perform an in-depth analysis of our studied models' performance on CAA using established machine learning interpretability techniques. The results of our analysis illustrate important findings that illuminate the behavior of LMs in understanding stylometric code patterns during the task of CAA, and point towards important directions for future work.

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