Abstract
Existing research on legacy system modernization has primarily focused on technical challenges. Can a system be modernized while concurrently enhancing business processes?
This paper introduces a strategic, four-step framework designed to guide software modernization in large organizations. This systematic approach provides a well-structured pathway towards modernization, targeting both cost reduction and efficiency enhancement. Strategically, the framework aligns with business goals and objectives to strengthen the modernization process and employs a Large Language Model to validate the approach.
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Acknowledgements
The authors would like to express their sincere gratitude to the Brazilian Aeronautics Institute of Technology (ITA) for their generous support of this research. To the ITA and its dedicated staff, we extend our deepest thanks for their indispensable support.
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Appendices
Appendix 1. Appendix Business Canvas Template
The image provided below, which represents an adapted version of the Business Model Canvas, was cited during the presentation of our framework. We include it here as it serves as an illustrative model for the implementation and comprehension of our proposed framework. The adaptation of this well-established tool elucidates the intricacies of our approach and provides a visual aid for further understanding.
Appendix 2. Appendix IT Canvas Template
The following image depicts the IT Model Canvas, which was referenced during the presentation of our framework and is thus included here as an exemplar model. This visualization offers both an incremental view and a final vision, providing a comprehensive understanding of our proposed approach. However, it is important to note that, despite having these comprehensive views, it is not necessarily obligatory to fill out every aspect initially. The model is designed to accommodate progressive elaboration, allowing for adaptability and flexibility in its utilization.
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Neto, W.C., Dias, L.A.V. (2024). Strategic Software Modernization: Business-IT Convergence with Large Language Models. In: Latifi, S. (eds) ITNG 2024: 21st International Conference on Information Technology-New Generations. ITNG 2024. Advances in Intelligent Systems and Computing, vol 1456. Springer, Cham. https://doi.org/10.1007/978-3-031-56599-1_29
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