الدراسة في بصمة المطور في بيئات البرمجة المدعومة بالذكاء الاصطناعي

Authors

  • عبيد ابوالقاسم التقازي قسم تقنيات المعلومات ، المعهد العالي للعلوم والتقنية الزهراء، المدينة الزهراء ، ليبيا Author
  • الصادق علي محمد العربي قسم تقنيات المعلومات ، المعهد العالي للعلوم والتقنية الزهراء، المدينة الزهراء ، ليبيا Author
  • هبه مصطفي ابوعائشة قسم تقنيات المعلومات ، المعهد العالي للعلوم والتقنية الزهراء، المدينة الزهراء ، ليبيا Author

Keywords:

Software development, AI-powered programming agents, software repository governance, distinct fingerprints

Abstract

Software development has undergone a radical transformation with the emergence of AI-powered programming agents such as GitHub Copilot, Cursor, and Cloud Code. This shift has led to new challenges related to software repository governance and the ability to trace developer contributions. This research aims to analyze the concept of the "developer fingerprint" within this new context by examining the unique behavioral signatures left by both human developers and AI agents during the writing and modification of code. The research relies on analyzing a set of characteristics extracted from pull requests to develop a machine learning model capable of accurately identifying the source of code. Preliminary results have shown that AI agents possess distinct fingerprints, opening the door to a deeper understanding of human-machine collaboration patterns and providing a foundation for new software governance mechanisms.

Downloads

Download data is not yet available.

References

1. Ghaleb, T. A. (2026). Fingerprinting AI Coding Agents on GitHub. arXiv preprint arXiv:2601.17406.

2. Tihanyi, N., et al. (2025). The Hidden DNA of LLM-Generated JavaScript: Structural Patterns Enable High-Accuracy Authorship Attribution. arXiv preprint arXiv:2510.10493.

3. Guo, J., et al. (2026). Code Fingerprints: Disentangled Attribution of LLM-Generated Code. arXiv preprint arXiv:2603.04212.

4. Horvath, M., et al. (2026). Bridging Behavioral Biometrics and Source Code Stylometry: A Survey of Programmer Attribution. arXiv preprint arXiv:2603.11150.

5. Awad, M. N. A., Ivanov, S., & Tikhonova, O. (2025). Pre-Filtering Code Suggestions using Developer Behavioral Telemetry to Optimize LLM-Assisted Programming. In 2025 40th IEEE/ACM International Conference on Automated Software Engineering Workshops (ASEW).

6. Dipongkor, A. K., et al. (2025). Reassessing Code Authorship Attribution in the Era of Language Models. arXiv preprint arXiv:2506.17120.

7. Borysenko, O. (2026). Developer Experience with AI Coding Agents: HTTP Behavioral Signatures in Documentation Portals. arXiv preprint arXiv:2604.02544.

8. AI IDEs or Autonomous Agents? Measuring the Impact of Coding Agents on Software Development. (2026). arXiv preprint arXiv:2601.13597.

9. Beyond the Commit: Developer Perspectives on Productivity with AI Coding Assistants. (2026). arXiv preprint arXiv:2602.03593

10. Michael B. James .(2022) Human-Computer Interaction (cs.HC); Programming Languages (cs.PL) arXiv:2206.15000 [cs.HC]

11. Majeed Kazemitabaar (2023) Human-Computer Interaction (cs.HC) arXiv:2302.07427 [cs.HC]

Downloads

Published

2026-03-26

How to Cite

الدراسة في بصمة المطور في بيئات البرمجة المدعومة بالذكاء الاصطناعي. (2026). Al-Farooq Journal of Sciences, 2(1), 1033-1043. https://afjs.histr.edu.ly/index.php/afjs/article/view/125