An Integrative Framework for Legacy System Evolution: Leveraging Multi-Criteria Decision-Making, Reverse Engineering, and Machine Learning for Architectural Transformation

Victoria Vance-Kauffman

Authors

Victoria Vance-Kauffman Department of Software Engineering and Information Systems, Zurich Institute of Applied Technology, Switzerland

Keywords:

Legacy Systems, Reverse Engineering, Multi-Criteria Decision-Making, Machine Learning

Abstract

The persistent reliance on legacy information systems presents a significant bottleneck for organizational agility and technological innovation in the modern digital era. This research explores the multidimensional challenges of migrating and evolving legacy systems through an integrative framework that combines classic software engineering principles with modern computational intelligence. By synthesizing the processes of technological innovation with advanced decision-making methodologies, such as the Analytic Network Process (ANP) and the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), this study addresses the complexities of architectural transformation. We investigate the role of reverse engineering and design recovery in reclaiming system semantics, alongside the application of machine learning-assisted service boundary detection for the modularization of monolithic codebases. The research provides a comprehensive analysis of the "disciplined evolution" approach, contrasting it with high-risk "cold turkey" migrations. Furthermore, the paper examines the socio-technical implications of integrating commercial and social financial models into institutional frameworks, illustrating the broader utility of multi-criteria decision support systems. The findings suggest that a successful transition requires a hybrid strategy involving encapsulation, data re-engineering, and incremental migration, guided by a robust consensus-building process among stakeholders.

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