AI-Guided Modernization Playbooks for Legacy Mission-Critical Payment Platforms
Keywords:
AI-guided modernization, legacy systems, payment platforms, dependency graphs, compliance timelinesAbstract
Legacy mission-critical payment infrastructures are under a lot of stress because they are becoming old, the laws are changing, and the dangers to security are getting worse. The objective of this research is to presents an AI-driven system that autonomously produces modernisation playbooks by the examination of architectural dependency graphs, security advisories, and compliance horizon data, with the help of these method modernisation processes first, looks at risks, and recommends phased migration pathways that employ graph embeddings to figure out semantic dependencies and generative reasoning.
Downloads
References
M. Fowler, Refactoring: Improving the Design of Existing Code, Addison-Wesley, 2018.
N. Medvidovic and R. N. Taylor, "A classification and comparison framework for software architecture description languages," IEEE Transactions on Software Engineering, vol. 26, no. 1, pp. 70–93, Jan. 2000.
S. G. Eick, T. L. Graves, A. F. Karr, J. S. Marron, and A. Mockus, "Visualizing software changes," IEEE Transactions on Software Engineering, vol. 28, no. 4, pp. 396–412, Apr. 2002.
J. Cabot, R. Clarisó, and D. Riera, "Automated compliance checking in model-driven engineering," Information and Software Technology, vol. 52, no. 5, pp. 480–494, May 2010.
A. Mosley, "Legacy system modernization: The role of software architecture," IEEE Software, vol. 24, no. 2, pp. 36–44, Mar.-Apr. 2007.
K. Czarnecki, S. Helsen, and U. W. Eisenecker, "Staged configuration through specialization and multilevel configuration of feature models," Software Process: Improvement and Practice, vol. 10, no. 2, pp. 143–169, 2005.
M. T. Ribeiro, S. Singh, and C. Guestrin, "Why should I trust you? Explaining the predictions of any classifier," in Proc. 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2016, pp. 1135–1144.
X. Zhu and H. Chen, "Machine learning in software engineering: An overview," Computer Science Review, vol. 1, no. 2, pp. 140–148, May 2007.
J. Yang, D. Xu, and M. R. Lyu, "Automated software migration via statistical machine translation," in Proc. 35th International Conference on Software Engineering (ICSE), 2013, pp. 481–490.
Z. Chen, D. Lo, and J. Jiang, "A survey on software defect prediction," IEEE Transactions on Software Engineering, vol. 45, no. 8, pp. 789–813, Aug. 2019.
R. Gupta, M. Pal, and A. Jalali, "Graph embedding for software engineering: Techniques, applications, and challenges," Journal of Systems and Software, vol. 163, 110525, Dec. 2020.
W. Hamilton, R. Ying, and J. Leskovec, "Inductive representation learning on large graphs," in Proc. 31st Conference on Neural Information Processing Systems (NeurIPS), 2017, pp. 1025–1035.
T. N. Kipf and M. Welling, "Semi-supervised classification with graph convolutional networks," in Proc. International Conference on Learning Representations (ICLR), 2017.
D. Jin, Z. Zheng, H. Zhang, and X. Peng, "Deep generative models for software code: A survey," IEEE Transactions on Neural Networks and Learning Systems, vol. 31, no. 11, pp. 4439–4456, Nov. 2020.
L. Wang, X. Yan, and H. Mei, "Automated program repair via generative neural networks," in Proc. 28th International Joint Conference on Artificial Intelligence (IJCAI), 2019, pp. 2441–2447.
S. R. Choudhury, S. K. Deb, and A. Roychoudhury, "Risk-based modernization of software systems: A survey," ACM Computing Surveys, vol. 52, no. 3, pp. 55:1–55:35, Jun. 2019.
J. Zhang, P. R. Lewis, and M. L. Bern, "Legacy system modernization: A state-of-the-art review," Software Quality Journal, vol. 24, no. 2, pp. 311–350, Jun. 2016.
B. Kitchenham, E. Mendes, and G. H. Travassos, "Cross versus within-company cost estimation studies: A systematic review," IEEE Transactions on Software Engineering, vol. 33, no. 5, pp. 316–329, May 2007.
S. L. Pfleeger and J. M. Atlee, Software Engineering: Theory and Practice, 4th ed., Pearson, 2010.
J. Cleland-Huang, G. A. Solberg, O. Gotel, and S. N. Fricker, "Requirements traceability: State of the art and future directions," in Proc. 14th IEEE International Requirements Engineering Conference, 2006, pp. 139–148.
Downloads
Published
Issue
Section
License

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