Bridging Behavioral Biometrics and Source Code Stylometry: A Survey of Programmer Attribution

2026-03-11

AI Summary

This study addresses the critical challenge of source code authorship identification in software engineering, security, and digital forensics. It presents a systematic literature review of 47 studies published between 2012 and 2025, offering the first integrated framework that unifies behavioral biometrics and code stylometry to capture both stylistic and behavioral features of programmers. Employing a systematic mapping approach combined with content analysis and clustering, the review reveals that existing research predominantly focuses on closed-world authorship attribution tasks, heavily relies on limited benchmark datasets, and exhibits significant gaps in authorship verification, open-world scenarios, and reproducibility. This work provides a structured synthesis of the field and offers methodological guidance for future research directions.

Technology Category

• Natural Language Processing: Code Generation / Program Synthesis from Natural Language
• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior
• Humans and AI: Other Foundations of Human Computation & AI

Application Category

• Web Mining and Content Analysis: Web data provenance, reliability, and authenticity
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Security and Privacy: Large-scale security measurements

Abstract

Programmer attribution seeks to identify or verify the author of a source code artifact using stylistic, structural, or behavioural characteristics. This problem has been studied across software engineering, security, and digital forensics, resulting in a growing and methodologically diverse set of publications. This paper presents a systematic mapping study of programmer attribution research focused on source code analysis. From an initial set of 135 candidate publications, 47 studies published between 2012 and 2025 were selected through a structured screening process. The included works are analysed along several dimensions, including authorship tasks, feature categories, learning and modelling approaches, dataset sources, and evaluation practices. Based on this analysis, we derive a taxonomy that relates stylistic and behavioural feature types to commonly used machine learning techniques and provide a descriptive overview of publication trends, benchmarks, programming languages. A content-level analysis highlights the main thematic clusters in the field. The results indicate a strong focus on closed-world authorship attribution using stylometric features and a heavy reliance on a small number of benchmark datasets, while behavioural signals, authorship verification, and reproducibility remain less explored. The study consolidates existing research into a unified framework and outlines methodological gaps that can guide future work. This manuscript is currently under review. The present version is a preprint.

Problem

• programmer attribution
• source code stylometry
• authorship identification
• behavioral biometrics
• software forensics

Innovation

• programmer attribution
• source code stylometry
• behavioral biometrics
• systematic mapping study
• authorship verification