Optimization And Detection In Software Engineering With AI And Digital Twins

The rapid improvement of Artificial Intelligence (AI), Digital Twins (DT), and Continuous Integration/Continuous Deployment (CI/CD) has significantly changed software engineering by enhancing automation, predictive capacities, and real-time decision-making. Digital Twins provide a virtual representation of software systems, facilitating real-time monitoring, performance optimization, and predictive maintenance, whereas AI-driven CI/CD automates the software delivery pipeline, enhancing reliability, efficiency, and speed. This paper explores the combination of AI and Digital Twins in software engineering, emphasizing their importance in software development, testing, anomaly detection, and deployment procedures. Artificial intelligence techniques, especially machine learning algorithms, improve software quality through automation testing, prediction of vulnerabilities, and optimization of maintenance activities. Moreover, AI-driven anomaly detection in digital twin environments facilitates proactive problem resolution and system enhancement. Despite these advancements, other problems and research deficiencies gaps, including the absence of real-time capable tools, the intricacy of switching from traditional engineering methodologies, and the interpretability of AI-driven anomaly detection models. Addressing these challenges necessitates additional study in AI-enhanced digital twin methods, empirical validation via industrial case studies, and the advancement of explainable AI for software engineering applications. Integrating AI, DT, and CI/CD enhances software engineering by increasing efficiency, minimizing errors, and improving adaptability. This paper presents the capabilities and constraints of these technologies, offering perspectives on future avenues for enhancing software engineering methodologies.

Keywords:

Software Engineering , Artificial Intelligence , Digital Twins , Machine Learning , Optimization