This study investigates the application of deep learning techniques in object-oriented software engineering to evaluate system stability. By analyzing object-oriented metrics, class relationships, and design structures, the model identifies patterns that influence long-term software reliability. The case study demonstrates how deep learning can improve stability prediction compared to traditional methods. These insights help developers detect design weaknesses early, enhance maintainability, and build more robust and scalable software systems.
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2022 2nd ICACITE Conference (pp. 1431–1435), IEEE.
https://doi.org/10.1109/ICACITE53722.2022.9823479