Aatishkumar Dhami
California State University Long Beach
Long Beach, CA 90840
Dr Munish Kumar
K L E F Deemed To Be University
Green Fields, Vaddeswaram, Andhra Pradesh 522302, India
Abstract
This paper investigates the potential of large language models (LLMs) to enhance programming productivity through domain-specific code generation. By integrating domain expertise with advanced machine learning techniques, our approach tailors LLMs to generate code that aligns closely with specialized application requirements. The study outlines a systematic framework for fine-tuning language models using domain-specific datasets, enabling the automated synthesis of accurate and efficient code. Experimental results demonstrate that this targeted methodology not only reduces development time and mitigates common coding errors but also improves overall code maintainability. Comparisons with traditional programming practices reveal that domain-adapted LLMs provide significant gains in both speed and reliability. The insights derived from this research contribute to a broader understanding of how artificial intelligence can be leveraged to streamline software development processes, ultimately offering a robust toolset for modern developers.
Keywords
Large Language Models, Domain-Specific Code Generation, Programming Productivity, Fine-Tuning, AI-Assisted Development, Software Automation, Domain Expertise Integration, Code Synthesis
References
- https://www.google.com/url?sa=i&url=https%3A%2F%2Farxiv.org%2Fhtml%2F2406.12513v1&psig=AOvVaw2NahM-EByRwIolPQzkn8up&ust=1739684523722000&source=images&cd=vfe&opi=89978449&ved=0CBQQjRxqFwoTCPDr0Iz8xIsDFQAAAAAdAAAAABAE
- https://www.google.com/url?sa=i&url=https%3A%2F%2Fwww.geeksforgeeks.org%2Fexploring-the-technical-architecture-behind-large-language-models%2F&psig=AOvVaw1OZBejTC2rad1iosJLGXvO&ust=1739684959376000&source=images&cd=vfe&opi=89978449&ved=0CBQQjRxqFwoTCNDHuNf9xIsDFQAAAAAdAAAAABAR
- Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., … & Amodei, D. (2020). Language models are few-shot learners. arXiv preprint arXiv:2005.14165.
- Chen, M., Tworek, J., Jun, H., Yuan, Q., de Oliveira Pinto, H. P., Kaplan, J., … & Sutskever, I. (2021). Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374.
- Kumar, P., Sharma, A., & Gupta, R. (2023). Integrating AI in IDEs: Enhancing developer productivity through automated code generation. In Proceedings of the International Conference on Software Engineering (pp. 124-132).
- Li, J., Sun, Y., & Xu, C. (2022). Domain-specific code generation using fine-tuned language models. Journal of Software Engineering Research, 16(3), 45-62.
- (2021). OpenAI Codex: AI-powered code generation. Retrieved from https://openai.com/blog/openai-codex/
- Smith, R., & Chen, L. (2023). Advances in domain-specific machine learning for software development. ACM Transactions on Software Engineering and Methodology, 32(2), 1-22.
- Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., & Polosukhin, I. (2017). Attention is all you need. In Advances in Neural Information Processing Systems (pp. 5998-6008).
- Radford, A., Narasimhan, K., Salimans, T., & Sutskever, I. (2018). Improving language understanding by generative pre-training. OpenAI Technical Report.
- Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019). 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 (pp. 4171-4186).
- Li, X., & Wang, Y. (2020). A survey on deep learning for code generation. IEEE Transactions on Software Engineering, 46(6), 657-674.
- Zhang, Q., Chen, H., & Li, D. (2021). Fine-tuning language models for domain-specific applications. Journal of Artificial Intelligence Research, 70, 155-174.
- Park, S., & Kim, J. (2022). Enhancing software development efficiency with AI-assisted tools. Journal of Software Engineering, 18(2), 123-134.
- Lee, M., & Smith, J. (2020). Evaluating AI-driven code generation: Challenges and opportunities. In Proceedings of the International Conference on Software Engineering (pp. 89-97).
- Thompson, R., & Garcia, M. (2021). Security and compliance in automated code generation systems. IEEE Transactions on Software Engineering, 47(8), 1560-1574.
- Nguyen, T., & Ho, P. (2023). Domain-specific adaptation of large language models for secure code generation. Journal of Artificial Intelligence Research, 68, 101-119.