DevOps Beyond Software: Establishing CI/CD Frameworks across Semiconductor and Cloud Engineering Lifecycles
DOI:
https://doi.org/10.63282/3050-9246.IJETCSIT-V7I3P103Keywords:
DevOps, Continuous Integration (CI), Continuous Delivery (CD), Semiconductor Engineering, Cloud Engineering, Design Automation, Infrastructure as Code (IAC), Digital Transformation, Engineering Lifecycle Management, Automation FrameworkAbstract
DevOps has changed the way we build and deploy software, focusing on collaboration, automation, continuous integration and continuous delivery (CI/CD). This allows enterprises to provide software faster, with higher quality and greater operational efficiency. While these ideas are frequently employed in traditional software engineering, their use in other technological fields is still limited, despite increasing complexity and increasing demands of speed and dependability. In this post we discuss how CI/CD approaches may be used outside of software development to semiconductor and cloud engineering life-cycles. Disconnected workflows, lengthy validation cycles, varied toolchains and segregated teams can hinder productivity and creativity. Semiconductor creation entails complex procedures of design, simulation, verification and fabrication, while cloud engineering demands constant provisioning of infrastructure, managing configurations, ensuring security and deploying services in ever-changing settings. Such issues point to the necessity for a unified methodology that can integrate automation, traceability and constant feedback across many engineering disciplines. In this work, we propose a comprehensive CI/CD solution, which merges software, semiconductor and cloud engineering processes in one DevOps-driven approach. This framework brings together test automation, versioning, artifact management, infrastructure orchestration, design validation and continuous monitoring into a single scalable engineering environment. This paper presents a realistic case study showing how the implementation in cross-functional teams, with the help of the standardized pipelines and integrated automation, facilitates cooperation, reduces cycle times, enhances quality assurance and accelerates delivery outcomes. The results suggest that the use of DevOps principles in non-traditional domains can yield significant benefits in terms of operational consistency, reduction of human work, and end-to-end visibility along the product life cycle. This study adds to the knowledge of enterprise-wide DevOps adoption by providing a viable roadmap to organizations that aspire to integrate engineering processes, break down domain silos and realize continuous innovation across physical and digital technology ecosystems.
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References
[1] Petrakis, K., Agorogiannis, E., Antonopoulos, G., Anagnostopoulos, T., Grigoropoulos, N., Veroni, E., & Alexopoulos, K. (2025). Enhancing DevOps practices in the IoT–Edge–Cloud continuum: Architecture, integration, and software orchestration demonstrated in the COGNIFOG framework. Software, 4(2), 10.
[2] Allenki, Shiva Santosh, and Amogh Sharma. “Troubleshooting Replication Lag and Ensuring Data Consistency in Distributed Systems”. American International Journal of Computer Science and Technology, vol. 7, no. 4, July 2025, pp. 116-28, https://doi.org/10.63282/3117-5481/AIJCST-V7I4P111.
[3] Babar, Z. (2024). A study of business process automation with DevOps: A data-driven approach to agile technical support. American Journal of Advanced Technology and Engineering Solutions, 4(04), 01-32.
[4] Muppaneni, R. K. (2024). Why More Organizations Are Moving from NetSuite to Dynamics 365. American International Journal of Computer Science and Technology, 6(4), 59-70. https://doi.org/10.63282/3117-5481/AIJCST-V6I4P106
[5] Parakala, A., & Padgett, P. (2025). When AI Acts: Opportunities and Risks of Agentic Systems. International Journal of Artificial Intelligence, Data Science, and Machine Learning, 6(4), 29-40. https://doi.org/10.63282/3050-9262.IJAIDSML-V6I4P105
[6] Kolawole, I., & Fakokunde, A. (2024). Improving software development with continuous integration and deployment for Agile DevOps in engineering practices. International Journal of Computer Applications Technology and Research, 14(01), 25-39.
[7] Takkalapally, D., & Takkellapally, M. R. (2024). AI-SynPerf: Synthetic Data Intelligence Framework for 5G Mobile Performance Simulation. International Journal of Emerging Trends in Computer Science and Information Technology, 5(1), 182-194. https://doi.org/10.63282/3050-9246.IJETCSIT-V5I1P118
[8] Shiramalla, R. (2025). Autonomous Component Lifecycle Management in Salesforce LWC using AI-driven Predictive Rendering. International Journal of Artificial Intelligence, Data Science, and Machine Learning, 6(1), 274-283. https://doi.org/10.63282/3050-9262.IJAIDSML-V6I1P132
[9] Chintagunta, S. K. (2023). Survey of Containerization, Orchestration, and CI/CD Integration on DevOps in Modern Software Development.
[10] Mittal, Tanvi, et al. "Deep Reinforcement Policy for Coordinating Cooperative Autonomous Vehicles at Highway Intersection Merges." 2025 International Conference on Electrical Engineering and Informatics (ICEEI). IEEE, 2025.
[11] Muppaneni, Rajarshi Krishna. “Low-Code Revolution: How Power Platform Extends Dynamics 365
[12] Muppaneni, R. K. (2023). Low-Code Revolution: How Power Platform Extends Dynamics 365 Capabilities. International Journal of Artificial Intelligence, Data Science, and Machine Learning, 4(3), 162-171. https://doi.org/10.63282/3050-9262.IJAIDSML-V4I3P119
[13] Rayaprolu, R. (2024). AI Enhanced Cloud DevOps and Automation. Journal of Artificial Intelligence, 4(1), 362-381.
[14] Muppaneni, K. (2022). Optimizing React Hooks for Efficient State and Side-Effect Management. American International Journal of Computer Science and Technology, 4(6), 44-55. https://doi.org/10.63282/3117-5481/AIJCST-V4I6P105
[15] Vppalapati, Mallikarjun, and Phani Kumar Talasila. “Correlated Independence: Why Redundant Storage Systems Share the Same Fate”. International Journal of Emerging Trends in Computer Science and Information Technology, vol. 3, no. 1, Mar. 2022, pp. 169-7, https://doi.org/10.63282/3050-9246.IJETCSIT-V3I1P119.
[16] Goli, S. R. (2022). Scaling AI in Manufacturing: Role of CI/CD Pipelines in Industrial Automation Platforms. Manufacturing: Role of CI/CD Pipelines in Industrial Automation Platforms (March 09, 2022).
[17] Gaddam, R. R. (2024). Vertex AI Agent Builder for Regulated Environments. American International Journal of Computer Science and Technology, 6(2), 50-62. https://doi.org/10.63282/3117-5481/AIJCST-V6I2P106
[18] Srigadde, Bapu Rao, and Bhavitha Guntupalli. “Tracking the Status of Long-Running Apex Methods in LWC”. International Journal of Emerging Research in Engineering and Technology, vol. 6, no. 1, Mar. 2025, pp. 147-5, https://doi.org/10.63282/3050-922X.IJERET-V6I1P118.
[19] Han, S., He, Y., & Ding, Y. (2020, December). Enable an open software defined mobility ecosystem through vec-of. In 2020 IEEE 20th International conference on software quality, reliability and security companion (QRS-C) (pp. 229-236). IEEE.
[20] Katangoori, Sivadeep, and Diganto Ghosh. “Programmatic Governance Using Policy-As-Code and ML for Dynamic Compliance Enforcement”. Los Angeles Journal of Intelligent Systems and Pattern Recognition, vol. 5, July 2025, pp. 96-125
[21] Takkalapally, D. (2025). 6GSyn: AI-Driven Synthetic Data Generation for Next-Generation Wireless Performance Evolution. International Journal of Emerging Trends in Computer Science and Information Technology, 6(1), 168-177. https://doi.org/10.63282/3050-9246.IJETCSIT-V6I1P120
[22] Chatterjee, S. U. (2024). AI in Healthcare: Augmented Cloud-Native ERP Framework Integrating Digital Payments with SAP HANA and Machine Learning. International Journal of Computer Technology and Electronics Communication, 7(6), 9797-9802.
[23] Muppaneni, K. (2022). Comparative Analysis of Client-Side Storage Mechanisms. International Journal of AI, BigData, Computational and Management Studies, 3(1), 171-182. https://doi.org/10.63282/3050-9416.IJAIBDCMS-V3I1P119
[24] Kumar Doodala, A. N. (2025). Continuous Compliance Testing in Healthcare IT Using Shift-Right QA Strategies. International Journal of Artificial Intelligence, Data Science, and Machine Learning, 6(1), 258-267. https://doi.org/10.63282/3050-9262.IJAIDSML-V6I1P130
[25] Nama, P., Reddy, P., & Pattanayak, S. K. (2024). Artificial intelligence for self-healing automation testing frameworks: Real-time fault prediction and recovery. Artificial Intelligence, 64(3S).
[26] Allenki, Shiva Santosh. “Troubleshooting Backup, Restore, and Data Export Failures in Relational Databases”. International Journal of AI, BigData, Computational and Management Studies, vol. 6, no. 2, May 2025, pp. 127-3, https://doi.org/10.63282/3050-9416.IJAIBDCMS-V6I2P115.
[27] Kinanen, O. (2024). Implementing Software Containers in DevOps Practices for Quantum Computing.
[28] Shiramalla, R., & Katangoori, S. (2025). Zero Trust Architecture for Salesforce LWC using Adaptive Authentication Models. American International Journal of Computer Science and Technology, 7(1), 123-135. https://doi.org/10.63282/3117-5481/AIJCST-V7I1P110
[29] Srigadde, Bapu Rao. “Maximizing AI Callout Time Using Visualforce Pages in Lightning Components”. International Journal of AI, BigData, Computational and Management Studies, vol. 6, no. 3, Aug. 2025, pp. 127-36, https://doi.org/10.63282/3050-9416.IJAIBDCMS-V6I3P115.
[30] Karvonen, T. (2017). Continuous software engineering in the development of software-intensive products: towards a reference model for continuous software engineering.
[31] Vppalapati, Mallikarjun. “The Storage Stack Nobody Draws: Cabling, Panels, and the Illusion of Isolation”. International Journal of Emerging Research in Engineering and Technology, vol. 3, no. 2, June 2022, pp. 211-20, https://doi.org/10.63282/3050-922X.IJERET-V3I2P121.
[32] Suryadevara, S. S. K., & Nakirikanti, S. (2023). Privacy-Preserving Personalization Using Federated Learning in AEM. International Journal of AI, BigData, Computational and Management Studies, 4(4), 190-199. https://doi.org/10.63282/3050-9416.IJAIBDCMS-V4I4P119
[33] Weiss, J., & Patt, D. (2022). Software Defines Tactics: Structuring Military Software Acquisitions for Adaptability and Advantage in a Competitive Era. Hudson Institute.
[34] Gaddam, Rohit Reddy, and Kalyan Krishna. "KFP v2 Artifact-Centric ML Pipeline Governance." International Journal of Artificial Intelligence, Data Science, and Machine Learning 4.2 (2023): 142-153.
[35] Parakala, A. (2025). Market Growth Insights (2017–2025+). American International Journal of Computer Science and Technology, 7(6), 25-36. https://doi.org/10.63282/3117-5481/AIJCST-V7I6P103
[36] Mukesh, A. (2024). AI-Powered Data Engineering Frameworks for Smart Manufacturing Quality Control. International Journal of Engineering & Extended Technologies Research (IJEETR), 6(6), 9189-9206.
[37] Katangoori, Sivadeep. “Streaming Feature Stores and Real-Time ML Inference on Cloud-Native Infrastructure”. Newark Journal of Human-Centric AI and Robotics Interaction, vol. 5, Jan. 2025, pp. 282-08
[38] Kumar Doodala, A. N. (2024). Validating UX consistency Across Omnichannel Platform. American International Journal of Computer Science and Technology, 6(6), 87-97. https://doi.org/10.63282/3117-5481/AIJCST-V6I6P109
[39] Ahn, J. K., Cho, K., Seo, K., Kim, H. J., & Kim, S. (2025). Comprehensive Analysis and Recommendation of Supply Chain Risk Management Framework for the Military Domain. IEEE Access.
[40] Pandiarajan, V. (2025). Integrated Business Innovation: Case Studies and Frameworks in Design Thinking and AI for Business Excellence. Taylor & Francis.
[41] Veershetty, G. (2019). From Legacy Back Office to Intelligent Utility Enterprise a Practitioner Case Study of SAP Cloud Transformation and Utility IT Landscape Modernization. American International Journal of Computer Science and Technology, 1(1), 23-27. https://doi.org/10.63282/3117-5481/AIJCST-V1I1P103
[42] Gajula, S. (2024). Adaptive zero trust architecture for securing financial microservices. Computer Fraud & Security, 2024(12), 643–655. https://doi.org/10.52710/cfs.845
[43] Gajula, S. (2024). Cybersecurity risk prediction using graph neural networks. Journal of Information Systems Engineering and Management.
[44] Gajula, S. (2025). Architectural transformation of legacy financial systems: a framework for microservices, cloud, and API integration. Int. J. Inform. Technol. Manag. Inform. Syst, 16(2), 1201-1218
[45] Sreenivasulu Gajula. (2025). Cloud Transformation in Financial Services: A Strategic Framework for Hybrid Adoption and Business Continuity. International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 11(2), 1244 1254. https://doi.org/10.32628/CSEIT25112464
[46] A. Suresh, "A Comprehensive Study on Auto - BI Systems using Generative AI for Scalable and Explainable Enterprise Analytics," 2026 6th International Conference on Expert Clouds and Applications (ICOECA), Bengaluru, India, 2026, pp. 1579-1585, doi: 10.1109/ICOECA68095.2026.11485569.
