Generative AI for Software Engineering: Large Language Model-Driven Code Generation with Safety and Trust Assessment in Enterprise Development

Authors

  • Udaya Kumar Reddy Veeramreddygari   Independent Researcher, Frisco, TX, USA

DOI:

https://doi.org/10.32628/CSEIT23906195

Keywords:

Large Language Models, Microservices, Spring Boot, Code Generation, Software Engineering, Enterprise Development, API Documentation, Design Patterns

Abstract

This paper explores how large language models (LLMs) can streamline microservice development using Spring Boot by automating boilerplate code, enhancing API documentation, and suggesting design patterns. The methodology integrates GPT-3.5 and Codex models with Spring Boot development workflows through custom IDE plugins and CI/CD pipeline integration. A comprehensive case study involving enterprise application development demonstrates significant productivity gains, with 40% reduction in development time and 25% improvement in code quality metrics. The study includes evaluation of generated code quality, documentation accuracy, and developer productivity across multiple microservice development scenarios. Results show that LLM-assisted development maintains high code quality while substantially reducing repetitive programming tasks, establishing a foundation for AI-augmented software engineering practices in enterprise environments.

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IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

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Published

2023-11-30

Issue

Volume 9, Issue 6, November-December-2023

Section

Research Articles

License

Copyright (c) IJSRCSEIT

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
Udaya Kumar Reddy Veeramreddygari , " Generative AI for Software Engineering: Large Language Model-Driven Code Generation with Safety and Trust Assessment in Enterprise Development" International Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 9, Issue 6, pp.569-582, November-December-2023. Available at doi : https://doi.org/10.32628/CSEIT23906195