The rapid evolution of artificial intelligence, particularly large language models (LLMs), has profoundly influenced software engineering practices. Software architecture design, microservice migration, code generation, quality assurance, and risk analysis are increasingly supported by intelligent systems capable of analyzing and generating complex software artifacts. Despite the growing body of literature addressing artificial intelligence in software engineering, a comprehensive synthesis that integrates architectural decision support, microservice recommendation, automated code analysis, and generative AI-driven development processes remains limited. This research presents an extensive multivocal literature-based investigation into the role of machine learning and large language models in modern software architecture and microservice engineering. Drawing upon a wide range of empirical studies, systematic literature reviews, and foundational research in software engineering, the study examines how AI technologies are transforming architectural design processes, system modularization, requirements engineering, and software quality management.

The research employs a qualitative analytical methodology informed by systematic literature review practices and grounded theory-inspired synthesis to interpret findings across existing academic works. The analysis investigates the theoretical foundations, architectural implications, and practical applications of generative AI and machine learning techniques in software engineering environments. Particular emphasis is placed on microservice recommendation systems, migration from monolithic architectures, architectural pattern identification, code generation systems, and automated software quality assessment. Furthermore, the research explores the implications of integrating knowledge graphs with LLMs for improved knowledge access in architectural decision-making.

Findings reveal that AI-driven development tools are reshaping the software engineering lifecycle by enabling automated reasoning over codebases, facilitating design pattern discovery, improving risk analysis, and supporting architectural decision-making. However, the study also identifies significant challenges related to reliability, governance, explainability, and integration with mission-critical systems. The results highlight both the transformative potential and the critical limitations of current AI-driven software engineering practices. The paper concludes by outlining future research directions aimed at improving the reliability, transparency, and architectural integration of AI technologies within complex software ecosystems.

Large language models, software architecture, microservices, machine learning in software engineering

Abdalkareem, R., Mujahid, S., Suhaib, M., Shihab, E. A machine learning approach to improve the detection of CI skip commits. IEEE Transactions on Software Engineering, 2020.

Abdeljaber, O., Avci, O., Kiranyaz, S., Gabbouj, M., Inman, D.J. Real-time vibration-based structural damage detection using one-dimensional convolutional neural networks. Journal of Sound and Vibration, 388, 2017, 154-170.

Abuhamad, M., AbuHmed, T., Mohaisen, A., Nyang, D. Large-scale and language-oblivious code authorship identification. Proceedings of the ACM SIGSAC Conference on Computer and Communications Security, 2018.

Abunadi, I., Alenezi, M. Towards cross project vulnerability prediction in open source web applications. International Conference on Engineering & MIS, 2015.

Aggarwal, S. Software code analysis using ensemble learning techniques. International Conference on Advanced Information Science and System, 2019.

Agnihotri, M., Chug, A. Application of machine learning algorithms for code smell prediction using object-oriented software metrics. Journal of Statistics and Management Systems, 23(7), 2020, 1159-1171.

Ahmad, W., Chakraborty, S., Ray, B., Chang, K.W. A transformer-based approach for source code summarization. Proceedings of the Annual Meeting of the Association for Computational Linguistics, 2020.

Ahmed, U.Z., Kumar, P., Karkare, A., Kar, P., Gulwani, S. Compilation error repair: For the student programs, from the student programs. International Conference on Software Engineering, 2018.

Al-Jamimi, H.A., Ahmed, M. Machine learning-based software quality prediction models: State of the art. International Conference on Information Science and Applications, 2013.

Al Qasem, O., Akour, M., Alenezi, M. The influence of deep learning algorithms factors in software fault prediction. IEEE Access, 8, 2020, 63945-63960.

Alazba, A., Aljamaan, H. Code smell detection using feature selection and stacking ensemble: An empirical investigation. Information and Software Technology, 138, 2021.

Aleem, S., Capretz, L.F., Ahmed, F. Comparative performance analysis of machine learning techniques for software bug detection. International Conference on Software Engineering and Applications, 2015.

Aleti, A., Martinez, M. E-APR: mapping the effectiveness of automated program repair techniques. Empirical Software Engineering, 26(5), 2021.

Ali, N., Sharafi, Z., Guéhéneuc, Y.G., Antoniol, G. An empirical study on the importance of source code entities for requirements traceability. Empirical Software Engineering, 20(2), 2015.

Alsayed, A.S., Dam, H.K., Nguyen, C. MicroRec: Leveraging large language models for microservice recommendation. Proceedings of the International Conference on Mining Software Repositories, 2024.

Bucaioni, A., Weyssow, M., He, J., Lyu, Y., Lo, D. Artificial intelligence for software architecture: Literature review and the road ahead. arXiv, 2025.

C, T. Aspect-oriented challenges in system integration with microservices, SOA and IoT. Enterprise Information Systems, 13, 2019, 467-489.

Corbin, J., Strauss, A. Basics of Qualitative Research: Techniques and Procedures for Developing Grounded Theory. SAGE Publications, 2008.

Dybå, T., Dingsøyr, T. Empirical studies of agile software development: A systematic review. Information and Software Technology, 50, 2008, 833-859.

Esposito, M., Palagiano, F., Lenarduzzi, V., Taibi, D. Beyond words: On large language models actionability in mission-critical risk analysis. Proceedings of the International Symposium on Empirical Software Engineering and Measurement, 2024.

Esposito, M., Palagiano, F., Lenarduzzi, V., Taibi, D. On large language models in mission-critical IT governance: Are we ready yet? arXiv, 2024.

Fan, A., Gokkaya, B., Harman, M., Lyubarskiy, M., Sengupta, S., Yoo, S., Zhang, J.M. Large language models for software engineering: Survey and open problems. IEEE/ACM International Conference on Software Engineering, 2023.

Garousi, V., Felderer, M., Mäntylä, M.V. Guidelines for including grey literature and conducting multivocal literature reviews in software engineering. Information and Software Technology, 106, 2019.

Gustrowsky, B., Villarreal, J.L., Alférez, G.H. Using generative artificial intelligence for suggesting software architecture patterns from requirements. Intelligent Systems and Applications. Springer, 2024.

K. S. Hebbar, “MACHINE LEARNING-ASSISTED SERVICE BOUNDARY DETECTION FOR MODULARIZING LEGACY SYSTEMS,” International Journal of Applied Engineering & Technology, vol. 04, no.02, pp. 401-414, Sep. 2022, https://romanpub.com/resources/ijaet-v4-2-2022-48.pdf

Hou, X., Zhao, Y., Liu, Y., Yang, Z., Wang, K., Li, L., Luo, X., Lo, D., Grundy, J., Wang, H. Large language models for software engineering: A systematic literature review. ACM Transactions on Software Engineering and Methodology, 33, 2024.

Jiang, J., Wang, F., Shen, J., Kim, S., Kim, S. A survey on large language models for code generation. arXiv, 2024.

Kaplan, A., Keim, J., Schneider, M., Koziolek, A., Reussner, R. Combining knowledge graphs and large language models to ease knowledge access in software architecture research. 2024.

Kitchenham, B., Brereton, P. A systematic review of systematic review process research in software engineering. Information and Software Technology, 55, 2013.

Kitchenham, B., Charters, S. Guidelines for performing systematic literature reviews in software engineering. 2007.

Lelovic, L., Huzinga, A., Goulis, G., Kaur, A., Boone, R., Muzrapov, U., Abdelfattah, A.S., Cerny, T. Change impact analysis in microservice systems: A systematic literature review. Journal of Systems and Software, 2024.

Marques, N., Silva, R.R., Bernardino, J. Using ChatGPT in software requirements engineering: A comprehensive review. Future Internet, 16, 2024.

Ozkaya, I. Application of large language models to software engineering tasks: Opportunities, risks, and implications. IEEE Software, 40, 2023.

Russo, D. Navigating the complexity of generative AI adoption in software engineering. ACM Transactions on Software Engineering and Methodology, 33, 2024.

Santos, P.d.O., Figueiredo, A.C., Moura, P.N., Diirr, B., Alvim, A.C., Santos, R.P.D. Impacts of the usage of generative artificial intelligence on software development process. Brazilian Symposium on Information Systems, 2024.

Saucedo, A., Rodríguez, G. Migration of monolithic systems to microservices using AI: A systematic mapping study. Congresso Ibero-Americano em Engenharia de Software, 2024.