Digital Twin-Based Predictive Analytics for Software Reliability: Simulating Real-World Scenarios for Performance Optimization
Keywords:
Fault Tolerance, Failure Prediction, Software Reliability, Simulation, Digital Twin, Predictive Analytics,, System Efficiency, Making Decisions Proactively and Using Real-Time DataAbstract
This paper introduces a Digital Twin-Based Predictive Analytics approach that combines
digital twins, predictive modeling, and real-time simulation to improve software performance
and dependability. By simulating real-world situations, the method predicts software faults,
improves fault tolerance, and guarantees effective system operation. Whereas failure prediction
uses a probability model to estimate possible breakdowns, reliability prediction uses an
exponential reliability function. A comparison analysis is used to evaluate performance,
showing that the maximum accuracy (95.67%), failure detection rate (92.7%), and system
performance efficiency (0.92 output/time) are obtained by integrating all three components.
The suggested approach reduces execution time while enhancing software resilience and
adaptability in comparison to conventional techniques. The results of the ablation study further
verify the role of each component, showing that digital twins, simulation, and predictive
modeling all work together to maximize execution performance and dependability. The Digital
Twin-Based Predictive Analytics model performs better than other predictive analytics
methods, as seen by its lowest error rate (0.05%), greatest dependability index (0.94), and
fastest processing time (2.4s). To promote proactive decision-making and scalable, reliable
software solutions, our findings highlight the importance of real-time adaptation in software
administration.
References
Liu, Z., Chen, W., Zhang, C., Yang, C., & Chu, H. (2019). Data super-network fault prediction model and maintenance strategy for mechanical product based on digital twin. Ieee Access, 7, 177284-177296.
Min, Q., Lu, Y., Liu, Z., Su, C., & Wang, B. (2019). Machine learning-based digital twin framework for production optimization in the petrochemical industry. International Journal of Information Management, 49, 502-519.
Karakra, A., Fontanili, F., Lamine, E., Lamothe, J., & Taweel, A. (2018, October). Pervasive computing integrated discrete event simulation for a hospital digital twin. In 2018 IEEE/ACS 15th International Conference on Computer Systems and Applications (AICCSA) (pp. 1-6). IEEE.
Zhuang, C., Liu, J., & Xiong, H. (2018). Digital twin-based smart production management and control framework for the complex product assembly shop floor. The international journal of advanced manufacturing technology, 96, 1149-1163.
Natarajan, D. R., & Kethu, S. S. (2019). Optimized cloud manufacturing frameworks for robotics and automation with advanced task scheduling techniques. International Journal of Emerging Technologies and Innovative Research, 7(4), 113. ISSN: 2347-3657.
Bobba, J., & Bolla, R. L. (2019). Next-gen HRM: AI, blockchain, self-sovereign identity, and neuro-symbolic AI for transparent, decentralized, and ethical talent management in the digital era. International Journal of HRM and Organizational Behavior, 7(4).
Gudivaka, R. K., Gudivaka, R. L., & Gudivaka, B. R. (2019). Robotics-driven swarm intelligence for adaptive and resilient pandemic alleviation in urban ecosystems: Advancing distributed automation and intelligent decision-making processes. International Journal of Modern Electronics and Communication Engineering (IJMECE), 7(4), 9.
Parthasarathy, K., & Ayyadurai, R. (2019). IoT-driven visualization framework for enhancing business intelligence, data quality, and risk management in corporate financial analytics. International Journal of HRM and Organizational Behavior, 7(3).
Ganesan, T., Veerappermal Devarajan, M., & Yalla, R. K. M. K. (2019). Performance analysis of genetic algorithms, Monte Carlo methods, and Markov models for cloud-based scientific computing. International Journal of Advanced Science and Engineering (IJASEM), 13(1), 17.
Natarajan, D. R., Narla, S., & Kethu, S. S. (2019). An intelligent decision-making framework for cloud adoption in healthcare: Combining DOI theory, machine learning, and multi-criteria approaches. International Journal of Engineering Research & Science & Technology, 15(3), 44.
Sareddy, M. R., & Hemnath, R. (2019). Optimized federated learning for cybersecurity: Integrating split learning, graph neural networks, and Hashgraph technology. International Journal of HRM and Organizational Behavior, 7(3).
Vasamsetty, C., Kadiyala, B., & Arulkumaran, G. (2019). Decision tree algorithms for agile ecommerce analytics: Enhancing customer experience with edge-based stream processing. International Journal of HRM and Organizational Behavior, 7(4).
Yalla, R. K. M. K., Yallamelli, A. R. G., & Mamidala, V. (2019). Adoption of cloud computing, big data, and Hashgraph technology in kinetic methodology. Journal of Current Science, 7(3). ISSN 9726-001X.
Boyapati, S. (2019). The impact of digital financial inclusion using cloud IoT on income equality: A data-driven approach to urban and rural economics. Journal of Current Science, 7(4), 7. ISSN 9726-001X.
Jadon, R. (2019). Enhancing AI-driven software with NOMA, UVFA, and dynamic graph neural networks for scalable decision-making. International Journal of Computer Science and Technology, 7(1), 64. ISSN 2347-3657.
Jadon, R. (2019). Integrating particle swarm optimization and quadratic discriminant analysis in AI-driven software development for robust model optimization. International Journal of Engineering Research & Science & Technology, 15(3), 25.
Devarajan, M. V. (2019). A comprehensive AI-based detection and differentiation model for neurological disorders using PSP Net and fuzzy logic-enhanced Hilbert-Huang transform. ISSN 2347-3657, 7(3), 94.
Nippatla, R. P. (2019). AI and ML-driven blockchain-based secure employee data management: Applications of distributed control and tensor decomposition in HRM. International Journal of Engineering Research & Science & Technology, 15(2).
Kadiyala, B. (2019). Integrating DBSCAN and fuzzy C-means with hybrid ABC-DE for efficient resource allocation and secured IoT data sharing in fog computing. International Journal of HRM and Organizational Behavior, 7(4).
Kethu, S. S. (2019). AI-enabled customer relationship management: Developing intelligence frameworks, AI-FCS integration, and empirical testing for service quality improvement. International Journal of HRM and Organizational Behavior, 7(2).
Dondapati, K. (2019). Lung cancer prediction using deep learning. International Journal of HRM and Organizational Behavior, 7(1).
Narla, S., Valivarthi, D. T., & Peddi, S. (2019). Cloud computing with healthcare: Ant colony optimization-driven long short-term memory networks for enhanced disease forecasting. International Journal of HRM and Organizational Behavior , 7(3).
Peddi, S., Narla, S., & Valivarthi, D. T. (2019). Harnessing artificial intelligence and machine learning algorithms for chronic disease management, fall prevention, and predictive healthcare applications in geriatric care. International Journal of Engineering Research & Science & Technology, 15(1).
Gudivaka, B. R. (2019). Big data-driven silicon content prediction in hot metal using Hadoop in blast furnace smelting. International Journal of Innovative Technology and Computer Engineering, 7(2), 32–49. https://doi.org/10.62646/ijitce.2019.v7.i2.pp32-49
Allur, N. S. (2019). Genetic algorithms for superior program path coverage in software testing related to big data. International Journal of Information Technology & Computer Engineering, 7(4). ISSN 2347–3657.
Peddi, S., Narla, S., & Valivarthi, D. T. (2018). Advancing geriatric care: Machine learning algorithms and AI applications for predicting dysphagia, delirium, and fall risks in elderly patients. ISSN 2347–3657, 6(4), 62.
Natarajan, D. R. (2018). A hybrid particle swarm and genetic algorithm approach for optimizing recurrent and radial basis function networks in cloud computing for healthcare disease detection. International Journal of Engineering Research & Science & Technology, 14(4).
Jadon, R. (2018). Optimized machine learning pipelines: Leveraging RFE, ELM, and SRC for advanced software development in AI applications. International Journal of Information Technology & Computer Engineering, ISSN 2347-3657, 6(1), 18.
Nippatla, R. P. (2018). A secure cloud-based financial analysis system for enhancing Monte Carlo simulations and deep belief network models using bulk synchronous parallel processing. International Journal of Emerging Technologies in Computational and Applied Sciences, 6(3), 89. ISSN 2347–3657.
