Enhancing DevOps with Machine Learning and Computer Vision
Automating Code Reviews and Bug Detection through Visual Analysis
Keywords:
DevOps, machine learning, computer vision, automated code review, bug detection, continuous integrationAbstract
In the ever-evolving landscape of software development, the integration of Machine Learning (ML) and Computer Vision (CV) into DevOps pipelines presents a transformative opportunity for automating code reviews and bug detection. This paper discusses the potential of leveraging these advanced technologies to enhance efficiency and accuracy within development cycles. By automating code reviews, teams can significantly reduce manual overhead while ensuring higher code quality. Furthermore, computer vision techniques can be utilized to analyze visual data, detecting bugs and anomalies in real-time. This paper provides an overview of the methodologies involved in implementing ML and CV within DevOps, explores the challenges faced, and presents case studies illustrating successful applications. The findings underscore the potential for these technologies to streamline development processes, thereby fostering innovation and improving collaboration among development teams.
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References
Gayam, Swaroop Reddy. "Deep Learning for Predictive Maintenance: Advanced Techniques for Fault Detection, Prognostics, and Maintenance Scheduling in Industrial Systems." Journal of Deep Learning in Genomic Data Analysis 2.1 (2022): 53-85.
George, Jabin Geevarghese, and Arun Rasika Karunakaran. "Enabling Scalable Financial Automation in Omni-Channel Retail: Strategies for ERP and Cloud Integration." Human-Computer Interaction Perspectives 1.2 (2021): 10-49.
Yellepeddi, Sai Manoj, et al. "AI-Powered Intrusion Detection Systems: Real-World Performance Analysis." Journal of AI-Assisted Scientific Discovery 4.1 (2024): 279-289.
Nimmagadda, Venkata Siva Prakash. "Artificial Intelligence for Supply Chain Visibility and Transparency in Retail: Advanced Techniques, Models, and Real-World Case Studies." Journal of Machine Learning in Pharmaceutical Research 3.1 (2023): 87-120.
Putha, Sudharshan. "AI-Driven Predictive Maintenance for Smart Manufacturing: Enhancing Equipment Reliability and Reducing Downtime." Journal of Deep Learning in Genomic Data Analysis 2.1 (2022): 160-203.
Sahu, Mohit Kumar. "Advanced AI Techniques for Predictive Maintenance in Autonomous Vehicles: Enhancing Reliability and Safety." Journal of AI in Healthcare and Medicine 2.1 (2022): 263-304.
Kondapaka, Krishna Kanth. "AI-Driven Predictive Maintenance for Insured Assets: Advanced Techniques, Applications, and Real-World Case Studies." Journal of AI in Healthcare and Medicine 1.2 (2021): 146-187.
Kasaraneni, Ramana Kumar. "AI-Enhanced Telematics Systems for Fleet Management: Optimizing Route Planning and Resource Allocation." Journal of AI in Healthcare and Medicine 1.2 (2021): 187-222.
Pattyam, Sandeep Pushyamitra. "Artificial Intelligence in Cybersecurity: Advanced Methods for Threat Detection, Risk Assessment, and Incident Response." Journal of AI in Healthcare and Medicine 1.2 (2021): 83-108.
Alluri, Venkat Rama Raju, et al. "Automated Testing Strategies for Microservices: A DevOps Approach." Distributed Learning and Broad Applications in Scientific Research 4 (2018): 101-121.
H. He, Y. Bai, E. Kanoulas, and C. S. Jensen, "Learning to rank from natural language questions," in Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, 2020, pp. 2532-2541.
J. Brownlee, Deep Learning for Computer Vision: Image Classification, Object Detection, and Face Recognition in Python. Melbourne, Australia: Machine Learning Mastery, 2019.
T. Chen, and C. Guestrin, "XGBoost: A scalable tree boosting system," in Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2016, pp. 785-794.
F. Chollet, Deep Learning with Python, 2nd ed. Greenwich, CT: Manning Publications, 2021.
G. E. Hinton et al., "Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups," IEEE Signal Processing Magazine, vol. 29, no. 6, pp. 82-97, Nov. 2012.
