Enhancing Model-Driven Reverse Engineering Using Machine Learning

Enhancing Model-Driven Reverse Engineering Using Machine Learning

연구 분야: Analysis

학회: 2024 IEEE/ACM 46th International Conference on Software Engineering: Companion Proceedings (ICSE-Companion)

Organizations often rely on large applications that are classified as legacy systems due to their dependence on outdated programming languages or platforms. To modernize these systems, it is necessary to understand their architecture, functionality, and business rules. Our research aims to define a novel model-driven reverse engineering (MDRE) approach to extract Unified Modeling Language (UML) and Object Constraint Language (OCL) representations from source code using Large Language Models (LLMs).