AI- Driven Legacy System Modernization: Hybrid Approaches for Business Logic Extraction, Modularization, and Code Migration
This approach advances AI-assisted legacy modernization: it formalizes LLM-based code comprehension to extract business logic, derive modular architectures and translate legacy code to verifiable modern programming languages, emphasizing explainability, correctness and cloud-ready security.
Projektverantwortliche:
Selina Lorch
Mitglied im Verbundkolleg: Juli 2025
Betreuender Hochschule Neu-Ulm:
Prof. Dr. Philipp Brune
Betreuender OTH-Amberg-Weiden:
Prof. Dr. Daniel Loebenberger
Selina Lorch
Hochschule Neu-Ulm Projekt: AI- Driven Legacy System Modernization: Hybrid Approaches for Business Logic Extraction, Modularization, and Code Migration
Publikationen:
Towards Trustworthy AI: Evaluating SHAP and LIME for Facial Emotion Recognition. Proceedings of the 58th Hawaii International Conference on System Sciences.
Lorch, S., Gebele, J., & Brune, P. (2025).