Validation-Gated GenAI Migration of Legacy Java to Cloud-Native Microservices
Keywords:
legacy Java migration; cloud-native microservices; generative AI code translation; syntactic-semantic gradient; validation-gated pipeline; distributed transactions; LLM correctnessAbstract
Legacy Java enterprise applications built on monolithic architectures resist automated cloud-native migration because large language model (LLM) translation pipelines optimize for syntactic fidelity while producing silent semantic errors at distributed-system boundaries. These errors, broken transaction semantics, violated consistency contracts, and misconfigured inter-service communication patterns do not surface in unit test suites and propagate unchecked into production environments. This paper introduces the Syntactic-Semantic Migration Gradient (SSMG), a formal gradient function G: T -> [0,1] that assigns each migration task type a semantic complexity score based on three weighted components: structural divergence (alpha = 0.20), distributed-system coupling (beta = 0.50), and contextual dependency (gamma = 0.30). The SSMG stratifies the migration task space into five tiers, from purely syntactic (G near 0) to distributed-semantic (G near 1). Built on this taxonomy, a five-stage validation-gated pipeline gates each LLM-generated artifact against formally specified pass criteria before promotion to the next stage. A six-dimension quantitative evaluation rubric, with thresholds informed by published empirical findings from CODEMENV (26.50% average pass@1 across seven LLMs; 43.84% for GPT-4o), Nguyen et al. (29-41% LLM semantic misclassification rate), Zhong et al. (34% coupling reduction under full contract coverage), and Garcia-Molina and Salem's saga atomicity theory, yields a Composite Migration Readiness Score (MRS) with a production threshold of MRS >= 0.87. The framework provides the first formal semantic stratification of the LLM migration task space and the first tier-weighted readiness metric for cloud-native Java migration, deployable with standard enterprise toolchains without new infrastructure investment.
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References
Garcia-Molina, H. and Salem, K., (1987) "Sagas," Proceedings of the 1987 ACM SIGMOD International Conference on Management of Data, pp. 249-259. Available: https://doi.org/10.1145/38713.38742.
Balalaie, A., Heydarnoori, A., and Jamshidi, P., (2016) "Microservices Architecture Enables DevOps: Migration to a Cloud-Native Architecture," IEEE Software, 33(3): 42-52. Available: https://doi.org/10.1109/MS.2016.64.
Jamshidi, P., Pahl, C., Mendonca, N. C., Lewis, J., and Tilkov, S., (2018) "Microservices: The Journey So Far and Challenges Ahead," IEEE Software, 35(3): 24-35. Available: https://doi.org/10.1109/MS.2018.2141039.
Mazlami, G., Cito, J., and Leitner, P., (2017) "Extraction of Microservices from Monolithic Software Architectures," Proceedings of the 2017 IEEE International Conference on Web Services (ICWS), pp. 524-531. Available: https://doi.org/10.1109/ICWS.2017.61.
Kuck, C. and Brune, P., (2026) "GenAI-Driven Migration of Legacy COBOL Applications to Clean Java Code," Smart Business Technologies (ICSBT 2025), Communications in Computer and Information Science, vol. 2666, Springer, Cham. Available: https://link.springer.com/chapter/10.1007/978-3-032-08614-3_4.
Gandhi, S., Patwardhan, M., Khatri, J., Vig, L., and Medicherla, R. K., (2024) "Translation of Low-Resource COBOL to Logically Correct and Readable Java Leveraging High-Resource Java Refinement," Proceedings of the 1st International Workshop on Large Language Models for Code, pp. 46-53. Available: https://dl.acm.org/doi/abs/10.1145/3643795.3648388.
Ibrahimzada, A. R. et al., (2025) "AlphaTrans: A Neuro-Symbolic Compositional Approach for Repository-Level Code Translation and Validation," Proceedings of the ACM International Conference on the Foundations of Software Engineering (FSE 2025). Available: https://arxiv.org/abs/2410.24117.
Chen, M. et al., (2021) "Evaluating Large Language Models Trained on Code," arXiv:2107.03374. Available: https://arxiv.org/abs/2107.03374.
Pearce, H., Ahmad, B., Tan, B., Dolan-Gavitt, B., and Karri, R., (2022) "Asleep at the Keyboard? Assessing the Security of GitHub Copilot's Code Contributions," 2022 IEEE Symposium on Security and Privacy (S&P), pp. 754-768. Available: https://doi.org/10.1109/SP46214.2022.9833571.
Tihanyi, N., Bisztray, T., Ferrag, M. A., Jain, R., and Cordeiro, L. C., (2023) "The FormAI Dataset: Generative AI in Software Security Through the Lens of Formal Verification," Proceedings of the 19th International Conference on Predictive Models and Data Analytics in Software Engineering (PROMISE 2023), ACM. Available: https://doi.org/10.1145/3617555.3617874.
Tihanyi, N., Ferrag, M. A., Bisztray, T., Jain, R., and Cordeiro, L. C., (2024) "How Secure is AI-Generated Code: A Large-Scale Comparison of Large Language Models," Empirical Software Engineering, Springer. Available: https://arxiv.org/abs/2404.18353.
Diggs, C. et al., (2024) "Leveraging LLMs for Legacy Code Modernization: Challenges and Opportunities for LLM-Generated Documentation," arXiv:2411.14971. Available: https://arxiv.org/abs/2411.14971.
Ou, Y. et al., (2025) "K3Trans: Evolving Triple Knowledge-Augmented LLMs for Code Translation in Repository Context," arXiv:2503.18305. Available: https://arxiv.org/abs/2503.18305.
Yuan, Z. et al., (2024) "TransAGENT: An LLM-Based Multi-Agent System for Code Translation," arXiv:2409.19894. Available: https://arxiv.org/abs/2409.19894.
Chang, W. et al., (2025) "SagaLLM: Context Management, Validation, and Transaction Guarantees for Multi-Agent LLM Planning," Proceedings of the VLDB Endowment (PVLDB), 18(12): 4874-4886. Available: https://doi.org/10.14778/3750601.3750611.
Cheng, K., Shen, X., Yang, Y., Wang, T., Cao, Y., Ali, M. A., Wang, H., Hu, L., and Wang, D., (2025) "CODEMENV: Benchmarking Large Language Models on Code Migration," Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (ACL 2025). Available: https://arxiv.org/abs/2506.00894.
Chen, X., Xue, J., Xie, X., Liang, C., and Ju, X., (2025) "A Systematic Literature Review on Neural Code Translation," arXiv:2505.07425. Available: https://arxiv.org/abs/2505.07425.
Rabbi, F., Saha, S. K., and Yang, J., (2025) "Beyond Translation Accuracy: Addressing False Failures in LLM-Based Code Translation," Proceedings of the 3rd ACM International Conference on AI-Powered Software (AIware 2025). Available: https://doi.org/10.1145/3805760.3814890.
Mohsin, M. A. et al., (2025) "On the Fundamental Limits of LLMs at Scale," arXiv:2511.12869. Available: https://arxiv.org/abs/2511.12869.
Ziftci, C. et al., (2025) "Migrating Code At Scale With LLMs At Google," Proceedings of the ACM International Conference on the Foundations of Software Engineering (FSE 2025), Industry Papers. Available: https://arxiv.org/abs/2504.09691.
Abgaz, Y., McCarren, A., Elger, P., Solan, D., Lapuz, N., Bivol, M., Jackson, G., Yilmaz, M., Buckley, J., and Clarke, P., (2023) "Decomposition of Monolith Applications Into Microservices Architectures: A Systematic Review," IEEE Transactions on Software Engineering, 49(8): 4213-4242. Available: https://doi.org/10.1109/TSE.2023.3287297.
Zhong, C., Li, S., Huang, H., Liu, X., Chen, Z., Zhang, Y., and Zhang, H., (2024) "Domain-Driven Design for Microservices: An Evidence-Based Investigation," IEEE Transactions on Software Engineering, 50(6): 1425-1449. Available: https://doi.org/10.1109/TSE.2024.3385835.
Trabelsi, I., Abdellatif, M., Abubaker, A., Moha, N., Mosser, S., Ebrahimi-Kahou, S., and Gueheneuc, Y. G., (2023) "From Legacy to Microservices: A Type-Based Approach for Microservices Identification Using Machine Learning and Semantic Analysis," Journal of Software: Evolution and Process, 35(10): e2503. Available: https://doi.org/10.1002/smr.2503.
Hou, X., Zhao, Y., Liu, Y., Yang, Z., Wang, K., Li, L., Luo, X., Lo, D., Grundy, J., and Wang, H., (2024) "Large Language Models for Software Engineering: A Systematic Literature Review," ACM Transactions on Software Engineering and Methodology, 33(8): 1-79. Available: https://doi.org/10.1145/3695988.
Wang, J., Huang, Y., Chen, C., Liu, Z., Wang, S., and Wang, Q., (2024) "Software Testing With Large Language Models: Survey, Landscape, and Vision," IEEE Transactions on Software Engineering, 50(4): 911-936. Available: https://doi.org/10.1109/TSE.2024.3368208.
Nguyen, T.-T., Vu, T. T., Vo, H. D., and Nguyen, S., (2025) "An Empirical Study on Capability of Large Language Models in Understanding Code Semantics," Information and Software Technology. Available: https://doi.org/10.1016/j.infsof.2025.107780.
Danyaro, K. U. et al., (2025) "LLM-Based Code Generation: A Systematic Literature Review With Technical and Demographic Insights," IEEE Access, 13: 194915-194939. Available: https://doi.org/10.1109/ACCESS.2025.3631952.
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