AI-Augmented Root Cause Analysis: Enhancing Debugging Efficiency in Large-Scale Software Systems
Keywords:
Artificial Intelligence (AI), Root Cause Analysis (RCA), Debugging Efficiency, Machine Learning (ML), Anomaly Detection, Causal Inference, Predictive Analytics, Cloud Computing, Self-Healing Systems, Quantum ComputingAbstract
Debugging large scale software systems is not an easy task, because of their nature of being distributed, complex, and also very much volume of generated data. Root Cause Analysis (RCA) with modern and cloud based microservice, real time application is always challenging to traditional RCA methods. A new highlight in RCA through the practical integration is AI, which incorporates into RCA automated anomaly detection, pattern recognition and predictive failure analysis. The core components of AI DRCA which include machine learning components, natural language processing (NLP) and causal inference techniques, all the while improving debugging efficiency and reduce system downtime are explored in this paper. It also goes over real-world AI powered RCA via enterprise environment, data quality, scalability and explain ability, and the coming trends with AI driven self-healing systems and quantum enhanced debugging. AI naturally solves a fundamental digital challenge: faster failure resolution, better software reliability and automation of the debugging process with its RCA capability, and now is an explicit essential in modern software maintenance.
References
Mohamed, K. S. (2023). Deep learning for spatial computing: augmented reality and metaverse “the Digital Universe”. In Deep Learning-Powered Technologies: Autonomous Driving, Artificial Intelligence of Things (AIoT), Augmented Reality, 5G Communications and Beyond (pp. 131-150). Cham: Springer Nature Switzerland.
Meyer, W., & Oosthuizen, R. (2023, June). Verification & Validation Methods for Complex AI-enabled Cyber-Physical Learning-Based Systems: A Systematic Literature Review. In 2023 IEEE International Conference on Engineering, Technology and Innovation (ICE/ITMC) (pp. 1-7). IEEE.
Holzinger, A., Malle, B., Kieseberg, P., Roth, P. M., Müller, H., Reihs, R., & Zatloukal, K. (2017). Towards the augmented pathologist: Challenges of explainable-ai in digital pathology. arXiv preprint arXiv:1712.06657.
Khosravi, H., Shum, S. B., Chen, G., Conati, C., Tsai, Y. S., Kay, J., ... & Gašević, D. (2022). Explainable artificial intelligence in education. Computers and education: artificial intelligence, 3, 100074.
Bernholdt, D. E., Cary, J., Heroux, M. A., & McInnes, L. C. (2021). Position papers for the ASCR workshop on the science of scientific-software development and use. US Department of Energy (USDOE), Washington DC (United States). Office of Science.
Murdock, J. M., & Carroll, E. R. (2021). Simplifying and Visualizing the Ontology of Systems Engineering Models (No. SAND2021-7079). Sandia National Lab.(SNL-NM), Albuquerque, NM (United States).
Hughes, R. T., Zhu, L., & Bednarz, T. (2021). Generative adversarial networks–enabled human–artificial intelligence collaborative applications for creative and design industries: A systematic review of current approaches and trends. Frontiers in artificial intelligence, 4, 604234.
Pi, Y. (2021). Machine learning in governments: Benefits, challenges and future directions. JeDEM-eJournal of eDemocracy and Open Government, 13(1), 203-219.
Strandberg, P. E. (2021). Automated system-level software testing of industrial networked embedded systems. Malardalen University (Sweden).
Øverdal, M. Ø. (2022). Harnessing Artificial Intelligence Capabilities Through Cloud Services–a Case Study of Inhibitors and Success Factors.
Hong, J. H. (2021). AI-Driven Threat Detection and Response Systems for Cybersecurity: A Comprehensive Approach to Modern Threats. Journal of Computing and Information Technology, 1(1).
Sobana, S., Prabha, S. K., Seerangurayar, T., & Sudha, S. (2022). Securing future autonomous applications using cyber-physical systems and the Internet of Things. In Handbook of Research of Internet of Things and Cyber-Physical Systems (pp. 81-148). Apple Academic Press.
Subramaniam, S., Raju, N., Ganesan, A., Rajavel, N., Chenniappan, M., Prakash, C., ... & Dixit, S. (2022). Artificial intelligence technologies for forecasting air pollution and human health: a narrative review. Sustainability, 14(16), 9951.
Taguma, M., Feron, E., & Lim, M. H. (2018). Future of education and skills 2030: Conceptual learning framework. Organization of Economic Co-operation and Development.
Glikson, E., & Woolley, A. W. (2020). Human trust in artificial intelligence: Review of empirical research. Academy of management annals, 14(2), 627-660.
Rogoz, R. D. (2023). Integrating AI-Driven Anomaly Detection with Blockchain for Enhanced Security in IoT Networks. Journal of Big Data and Smart Systems, 4(1).
Barenkamp M., Rebstadt J., Thomas O., (2020) Applications of AI in classical software engineering
Rodrigue A., (2023) Root Cause Analysis: Boosting Your Factory’s Operations
(Originally published: July 28, 2023 Updated: July 29, 2023) The Importance and Best Practices of Software Maintenance
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