Cognitive Automation and Machine Learning-Based DevOps Optimization Model for Next-Generation Enterprise Software Engineering and Operations
Keywords:
Cognitive Automation, DevOps Optimization, Machine Learning, Enterprise Software EngineeringAbstract
The rapid evolution of enterprise software engineering has intensified the demand for adaptive, intelligent, and self-optimizing DevOps ecosystems capable of handling complex deployment pipelines, heterogeneous infrastructures, and continuous operational scaling. This research proposes a Cognitive Automation and Machine Learning-Based DevOps Optimization Model (CALM-DOOM) designed to enhance automation intelligence, predictive maintenance, and deployment efficiency in next-generation enterprise environments. The study integrates cognitive computing principles, machine learning architectures, and robotic process automation (RPA) paradigms to construct a unified optimization framework for software delivery and operational governance.
The methodology synthesizes insights from deep learning systems (Goodfellow et al., 2016), natural language processing advancements (Hirschberg & Manning, 2015), intelligent automation frameworks (IEEE Corporate Advisory Group, 2017), and industrial RPA implementations (Lacity& Willcocks, 2016). A key conceptual anchor is derived from enterprise automation transformations demonstrated in real-world deployments such as internal RPA capability scaling (Hallikainen, Bekkhus, & Pan, 2018), which is referenced throughout the study to contextualize operational automation maturity models.
The proposed model introduces a multi-layer architecture consisting of cognitive decision engines, predictive ML modules, and adaptive DevOps orchestration layers. Findings indicate that integrating cognitive feedback
loops with ML-driven observability significantly improves deployment success rates, reduces system downtime, and enhances adaptive scaling capabilities. The research contributes a structured theoretical and operational framework for intelligent DevOps transformation and provides actionable insights for enterprise-level automation adoption
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