Democratizing Code: How GPT and Large Language Models Are Reshaping the Landscape of Software Creation

Prakash Raj Ojha

doi:10.32628/CSEIT241051031

Authors

Prakash Raj Ojha Georgia Institute of Technology, USA Author

DOI:

https://doi.org/10.32628/CSEIT241051031

Keywords:

Generative Pre-trained Transformers, Software Development, Large Language Models, AI-Assisted Programming, Code Generation

Abstract

This article examines the transformative impact of Generative Pre-trained Transformers (GPT) and Large Language Models (LLMs) on software development practices. Through a comprehensive analysis of current literature and industry applications, we investigate how these AI-driven technologies are revolutionizing code generation, debugging, and overall development workflows. Our findings indicate that GPT and LLMs significantly enhance programmer productivity by automating routine tasks, providing real-time code suggestions, and facilitating rapid prototyping. Moreover, these models demonstrate potential in democratizing software development by lowering entry barriers for non-experts. However, the integration of AI in development processes also raises important ethical considerations and challenges, including potential biases in code generation and the changing nature of programming skills. This research contributes to the growing body of knowledge on AI-assisted software engineering and provides insights into the future trajectory of the field, suggesting that the symbiosis between human developers and AI models will likely define the next era of software development.

Downloads

Download data is not yet available.

References

A. Vaswani et al., "Attention is All You Need," in Advances in Neural Information Processing Systems, 2017, pp. 5998-6008. Available: https://papers.nips.cc/paper/2017/file/3f5ee243547dee91fbd053c1c4a845aa-Paper.pdf

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

J. Devlin, M. Chang, K. Lee, and K. Toutanova, "BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding," in Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), 2019, pp. 4171-4186. Available: https://aclanthology.org/N19-1423/

J. Kaplan et al., "Scaling Laws for Neural Language Models," arXiv preprint arXiv:2001.08361, 2020. Available: https://arxiv.org/abs/2001.08361

M. Allamanis, et al., "A Survey of Machine Learning for Big Code and Naturalness," ACM Computing Surveys, vol. 51, no. 4, pp. 1-37, 2018. Available: https://doi.org/10.1145/3212695 DOI: https://doi.org/10.1145/3212695

N. Ziegler, et al., "The Impact of AI on Developer Productivity: Evidence from GitHub Copilot," arXiv preprint arXiv:2302.06590, 2023. Available: https://arxiv.org/abs/2302.06590

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. Available: https://doi.org/10.1145/3340544 DOI: https://doi.org/10.1145/3340544

A. Svyatkovskiy, et al., "IntelliCode Compose: Code Generation Using Transformer," in Proceedings of the 2020 ACM SIGSOFT International Symposium on Foundations of Software Engineering (FSE '20), 2020, pp. 1433–1443. Available: https://doi.org/10.1145/3368089.3417058 DOI: https://doi.org/10.1145/3368089.3417058

E. A. AlOmar, et al., "Finding the Needle in a Haystack: On the Automatic Identification of Accessibility User Reviews," in Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems, 2021, pp. 1-15. Available: https://doi.org/10.1145/3411764.3445281 DOI: https://doi.org/10.1145/3411764.3445281

N. Papernot, et al., "SoK: Security and Privacy in Machine Learning," in 2018 IEEE European Symposium on Security and Privacy (EuroS&P), 2018, pp. 399-414. Available: https://doi.org/10.1109/EuroSP.2018.00035 DOI: https://doi.org/10.1109/EuroSP.2018.00035

A. Hindle, et al., "On the Naturalness of Software," in Communications of the ACM, vol. 59, no. 5, 2016, pp. 122-131. Available: https://doi.org/10.1145/2902362 DOI: https://doi.org/10.1145/2902362

M. Harman, et al., "Search Based Software Engineering: Techniques, Taxonomy, Tutorial," in Empirical Software Engineering and Verification, 2012, pp. 1-59. Available: https://link.springer.com/chapter/10.1007/978-3-642-25231-0_1 DOI: https://doi.org/10.1007/978-3-642-25231-0_1

S. Amershi, et al., "Software Engineering for Machine Learning: A Case Study," in 2019 IEEE/ACM 41st International Conference on Software Engineering: Software Engineering in Practice (ICSE-SEIP), 2019, pp. 291-300. Available: https://doi.org/10.1109/ICSE-SEIP.2019.00042 DOI: https://doi.org/10.1109/ICSE-SEIP.2019.00042