Enhancing Model-Driven Reverse Engineering Using Machine Learning.
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| Titel: | Enhancing Model-Driven Reverse Engineering Using Machine Learning. |
|---|---|
| Autoren: | Siala, Hanan Abdulwahab |
| Quelle: | ICSE: International Conference on Software Engineering; 2024, p173-175, 3p |
| Schlagwörter: | MACHINE learning, REVERSE engineering, UNIFIED modeling language, SOURCE code, LANGUAGE models |
| Abstract: | Organizations often rely on large applications that are classified as legacy systems due to their dependence on outdated programming languages or platforms.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). [ABSTRACT FROM AUTHOR] |
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| Datenbank: | Complementary Index |
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