Leveraging Generative Artificial Intelligence and Retrieval-Augmented Generation to Improve Software Engineering, Automated Code Generation, and Intelligent Debugging Systems
Main Article Content
Abstract
Generative Artificial Intelligence (GenAI) has emerged as a transformative technology for improving software engineering by automating code generation, software maintenance, and debugging tasks. However, standalone large language models often suffer from hallucination, outdated knowledge, and limited contextual understanding, reducing the reliability of generated code. Retrieval-Augmented Generation (RAG) addresses these limitations by integrating external knowledge repositories with generative models to produce context-aware, accurate, and verifiable software solutions. This study examines the integration of GenAI and RAG within software engineering workflows, emphasizing their applications in automated code generation, intelligent debugging, software documentation, and DevOps automation. The paper synthesizes existing literature to propose a conceptual framework demonstrating how retrieval-enhanced generation can improve code quality, reduce debugging time, strengthen semantic understanding of software artifacts, and support secure software development practices. Furthermore, the study discusses implementation challenges, including knowledge integration, security, explainability, and computational complexity. The findings suggest that combining Generative AI with Retrieval-Augmented Generation provides a promising foundation for developing reliable, intelligent, and scalable software engineering systems capable of supporting modern software development environments.
Article Details
Section
How to Cite
References
1. Berthelsen, M. (2015). The Role of Generative AI in the Modern DevOps Pipeline.
2. Parvez, M. R. (2022). Learning through Auxiliary Supervision for Multi-modal Low-resource Natural Language Processing. University of California, Los Angeles.
3. Balaganski, A. (2015). API Security Management. KuppingerCole Report, 70958, 20-27.
4. FU, R., ZHANG, Y., ZHANG, S., WU, X., GU, W., WANG, F., ... & ZHANG, Y. (2018). Collaborative Knowledge Distillation and Reinforcement Learning for Automated Ticket Triage in Large-Scale Production Systems.
5. Mahmoud, A., & Niu, N. (2015). On the role of semantics in automated requirements tracing. Requirements Engineering, 20(3), 281-300.
6. Li, J., Wang, Z., Garijo, D., & Poveda-Villalón, M. (2022). MASEO: A Multi-Agent System for Explainable Ontology Generation.
7. Gruetzemacher, R., & Paradice, D. (2022). Deep transfer learning & beyond: Transformer language models in information systems research. ACM Computing Surveys (CSUR), 54(10s), 1-35.
8. Sanghi, N. (2021). Deep reinforcement learning with python. New York, NY, USA: Apress.
9. Menick, J., Trebacz, M., Mikulik, V., Aslanides, J., Song, F., Chadwick, M., ... & McAleese, N. (2022). Teaching language models to support answers with verified quotes. arXiv preprint arXiv:2203.11147.
10. Cheng, X. (1997). education experience (Doctoral dissertation, Institute of Contemporary International Relations (CICIR), Beijing. University).
11. Kahn, V., Hopmeier, M. J., & Lara, J. A. (1985). Senior Design Project. Mechanical Engineering, University of Florida.
12. Nikolić, P., & Liu, R. (2021). Metaphysics of the machines: From human-robot-robot interaction to AI philosophers abstraction. Artnodes: revista de arte, ciencia y tecnología, (28), 6.
13. Design, I. I. (2012). Technologies. Retrieved, 12, 20.
14. Verma, A. (2025). Blockchain for Cyber Security: Enhancing Data Integrity and Trust in Digital Transactions.
15. Kola, J. N. (2017). Data Warehousing And Text Analytics As Instruments Of Cultural Knowledge Management: Implications For Digital Preservation And Societal Decision-Making. Power System Protection and Control, 45(1), 11-15.
16. Ezeagwuna, D. (2022). Measuring Return on Investment (ROI) in Enterprise Service Management: The Role of Service Now in Enhancing Organizational Efficiency and Cost Reduction. ADHYAYAN: A JOURNAL OF MANAGEMENT SCIENCES, 12(02), 59-71.
17. Kazmi, S. Chemical Upcycling of Polyethylene Terephthalate (PET) Waste into High-Value Functional Polymers.
18. Naidu, K. J. (2013). Performance Optimization Of ETL Pipelines In Distributed Data Warehouse Environments: A Network-Aware Scheduling Approach. International Journal of Advance Industrial Engineering, 1(03), 63-67.
19. Ravikumar, V. (2014). Fair and optimal resource allocation in wireless sensor networks.
20. Naidu, K. J. (2014). Secure OLAP Reporting Architectures: Integrating Role-based Access Control and Query Execution Plan Optimization for Enterprise Analytical Environments. SAMRIDDHI: A Journal of Physical Sciences, Engineering and Technology, 5(02), 155-159.
21. Kola, J. N. (2011). An Integrated Framework for Data Mining and Distributed Database Optimization in Resource-Constrained Network Environments. SAMRIDDHI: A Journal of Physical Sciences, Engineering and Technology, 2(02), 82-86.
22. Das, P. K., Kashem, M. A., Ferdus, Z., & Islam, S. (2019, October). Development and application of a new computerized smell generating system. In 2019 Global Conference for Advancement in Technology (GCAT) (pp. 1-5). IEEE.
23. Ferdus, M. Z., Khan, M. N. I., Islam, S., & Kashem, M. A. (2019, October). VFLT: SQA Model for Cyber Physical System. In 2019 Global Conference for Advancement in Technology (GCAT) (pp. 1-4). IEEE.
24. Islam, S. J., Islam, S., Ferdus, M. Z., Khan, M. N. I., Kashem, M. A., & Islam, M. S. (2020, September). Load compactness and recognizing area aware cluster head selection of wireless sensor networks. In 2020 International conference on computing and information technology (ICCIT-1441) (pp. 1-4). IEEE.
25. Ferdus, M. Z., Islam, S., & Kashem, M. A. (2019, October). An innovative load balancing cluster composition of wireless sensor networks. In 2019 global conference for advancement in technology (GCAT) (pp. 1-4). IEEE.
26. Islam, S., Khan, M. N. I., Ferdus, M. Z., Islam, S. J., & Kashem, M. A. (2020, September). Improving throughput using cooperating TDMA scheduling of wireless sensor networks. In 2020 International Conference on Computing and Information Technology (ICCIT-1441) (pp. 1-4). IEEE.
27. Ameperosa, E., & Bhounsule, P. A. (2020). Domain randomization using deep neural networks for estimating positions of bolts. Journal of Computing and Information Science in Engineering, 20(5), 051006.
28. Shorten, C., Khoshgoftaar, T. M., & Furht, B. (2021). Deep Learning applications for COVID-19. Journal of big Data, 8(1), 18.
29. Verma, A. QUANTIFYING ZERO TRUST: DEVELOPING GRC METRICS FOR MATURE CLOUD ENVIRONMENTS.
30. Muresan, S., Nakov, P., & Villavicencio, A. (2022, May). Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers).
31. Holmes, W. (2020). Artificial intelligence in education. In Encyclopedia of education and information technologies (pp. 88-103). Cham: Springer International Publishing.
32. Verma, A. The Quantum Leap For Grc: Transitioning To Crypto-Agility In Cloud Infrastructure.