Reverse Engineering GitHub CoPilot: Creating an OpenAI-Compatible Endpoint for Enhanced Developer Integration

Authors

  • Nur Arifin Akbar Universita Degli Studi di Palermo, Italy
  • Ardian Webi Krida Universiti Brunei Darussalam, Brunei
  • Akbar Setiawan STMIK Widya Utama, Purwokerto, Indonesia

DOI:

https://doi.org/10.24076/intechnojournal.2024v6i2.1895

Keywords:

Reverse Engineering, OpenAI

Abstract

This paper presents the reverse engineering of GitHub CoPilot to develop an OpenAI-compatible endpoint, enabling broader access and integration possibilities for AI-assisted code completion. By analyzing CoPilot's communication protocols and creating a proxy server that translates OpenAI API requests to CoPilot's internal API, we bridge the gap between proprietary tools and open standards. The implementation, allows developers to utilize CoPilot's capabilities within their preferred environments using the familiar OpenAI API interface. We detail the system architecture, authentication mechanisms, request processing pipeline, and performance optimization techniques. Our results demonstrate successful integration, with robust performance metrics, including low response times and high compatibility rates. This work opens avenues for enhanced developer productivity and flexibility in AI-assisted coding tools.

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References

Chen, M., et al. ”Evaluating Large Language Models Trained on Code.” arXiv preprint arXiv:2107.03374, 2021.

Brown, T.B., et al. ”Language Models are Few-Shot Learners.” Advances in Neural Information Processing Systems, 2020.

Xu, X., et al. ”A Systematic Review of AI-Assisted Code Generation.” IEEE Transactions on Software Engi- neering, 2022.

Ziegler, A., et al. ”Productivity Assessment of Neural Code Completion.” International Conference on Software Engineering, 2022.

Tabachnyk, D., et al. ”An Empirical Study of GitHub Copilot’s Impact on Developer Productivity.” Journal of Systems and Software 2022.

Liu, H., et al. ”A Survey of Large Language Models for Code Generation.” ACM Computing Surveys, 2023.

Wang, S., et al. ”Security Analysis of AI-Assisted Code Generation Tools.” IEEE Security Privacy, 2021.

Zhang, T., et al. ”A Survey on Neural Program Synthesis.” ACM Computing Surveys, 2021.

Papernot, N., et al. ”Security and Privacy in Machine Learning.” IEEE Security Privacy, 2018.

Hardt, D. ”The OAuth 2.0 Authorization Framework.” RFC 6749, 2022.

Johnson, R., et al. ”Security Analysis of Token-Based Authentication Systems.” Journal of Cybersecurity, 2023.

Smith, A., et al. ”Advanced Techniques for API Request Validation.” IEEE Software, 2024.

Wilson, M., et al. ”A Study of Rate Limiting Strategies in Modern APIs.” International Conference on Web Services, 2023.

Brown, J., et al. ”Challenges in OAuth 2.0 Implementation for Modern Web Services.” ACM Security Conference, 2024.

Davis, K., et al. ”Design Patterns for API Translation and Compatibility.” IEEE Software Architecture, 2023.

Miller, S., et al. ”Caching Strategies for High-Performance API Services.” Performance Evaluation Review, 2024.

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Published

2024-12-31

Issue

Section

Articles