AI Copilot for the Modern Developer : Leveraging GenAI in Software Development

Authors

  • Anuja Nagpal University of South Florida, USA Author

DOI:

https://doi.org/10.32628/CSEIT241051056

Keywords:

AI Copilots, Generative AI, Software Development, Large Language Models, Reinforcement Learning

Abstract

This paper explores the transformative impact of AI copilots on software development, powered by Generative AI and Large Language Models. It examines how these tools enhance developer productivity, improve code quality, and democratize software development. The study delves into the core technologies behind AI copilots, including code synthesis and contextual learning, and discusses their integration with development environments. The paper also investigates the role of reinforcement learning in continuously improving AI copilots and analyzes their impact on software development practices. Additionally, it explores future prospects, including more sophisticated natural language understanding and improved cross-language support. Throughout, the paper emphasizes how AI copilots are reshaping the landscape of software engineering, potentially leading to more efficient, higher-quality, and innovative software solutions.

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References

A. Ziegler et al., "Productivity Assessment of Neural Code Completion," 2022 IEEE/ACM 44th International Conference on Software Engineering: Software Engineering in Practice (ICSE-SEIP), 2022, pp. 319-328. [Online]. Available: https://arxiv.org/abs/2205.06537#:~:text=View%20a%20PDF%20of%20the%20paper%20titled%20Productivity

Stack Overflow, "2023 Developer Survey," 2023. [Online]. Available: https://survey.stackoverflow.co/2023/

M. Chen et al., "Evaluating Large Language Models Trained on Code," arXiv preprint arXiv:2107.03374, 2021. [Online]. Available: https://arxiv.org/abs/2107.03374

Z. Li et al., "Improving Code Autocompletion with Transfer Learning," in 2022 IEEE/ACM 44th International Conference on Software Engineering (ICSE), 2022, pp. 1902-1913. [Online]. Available: https://ieeexplore.ieee.org/document/9793983

A. Svyatkovskiy et al., "IntelliCode Compose: Code Generation Using Transformer," in 2020 IEEE/ACM 42nd International Conference on Software Engineering (ICSE), 2020, pp. 1433-1443. [Online]. Available: https://arxiv.org/abs/2005.08025#:~:text=It%20leverages%20state-of-the-art%20generative%20transformer%20model%20trained%20on

J. Zhang, X. Wang, H. Zhang, H. Sun, K. Wang and X. Liu, "A Novel Neural Source Code Representation Based on Abstract Syntax Tree," in 2019 IEEE/ACM 41st International Conference on Software Engineering (ICSE), 2019, pp. 783-794. [Online]. Available: https://doi.org/10.1109/ICSE.2019.00086 DOI: https://doi.org/10.1109/ICSE.2019.00086

M. Tufano et al., "An Empirical Study on Learning Bug-Fixing Patches in the Wild via Neural Machine Translation," ACM Transactions on Software Engineering and Methodology, vol. 28, no. 4, pp. 1-29, 2019. [Online]. Available: https://doi.org/10.1145/3340544 DOI: https://doi.org/10.1145/3340544

X. Liu et al., "Neural-Machine-Translation-Based Commit Message Generation: How Far Are We?," in 2018 33rd IEEE/ACM International Conference on Automated Software Engineering (ASE), 2018, pp. 373-384. [Online]. Available: https://doi.org/10.1145/3238147.3238190 DOI: https://doi.org/10.1145/3238147.3238190

M. Li et al., "DeepCoder: Learning to Write Programs," in 5th International Conference on Learning Representations (ICLR), 2017. [Online]. Available: https://openreview.net/forum?id=ByldLrqlx

B. Roziere et al., "Unsupervised Translation of Programming Languages," Advances in Neural Information Processing Systems, vol. 33, pp. 20601-20611, 2020. [Online]. Available: https://proceedings.neurips.cc/paper/2020/hash/ed23fbf18c2cd35f8c7f8de44f85c08d-Abstract.html

A. Svyatkovskiy et al., "Fast and Memory-Efficient Neural Code Completion," in 2020 IEEE/ACM 42nd International Conference on Software Engineering: Companion Proceedings (ICSE-Companion), 2020, pp. 251-252. [Online]. Available: https://ieeexplore.ieee.org/document/9463109

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Published

01-11-2024

Issue

Section

Research Articles

How to Cite

[1]
Anuja Nagpal, “AI Copilot for the Modern Developer : Leveraging GenAI in Software Development”, Int. J. Sci. Res. Comput. Sci. Eng. Inf. Technol, vol. 10, no. 5, pp. 702–711, Nov. 2024, doi: 10.32628/CSEIT241051056.

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