DevOps and Continuous Delivery: Enhancing Agility in Software Engineering
DOI:
https://doi.org/10.15662/IJARCST.2024.0706001Keywords:
DevOps, Continuous Delivery, Agile Software Engineering, Infrastructure as Code, Automation, Software Deployment, Pipeline Optimization, DevSecOps, 2023 TrendsAbstract
In the evolving landscape of software engineering, DevOps and Continuous Delivery (CD) have emerged as pivotal methodologies to accelerate software development cycles, enhance product quality, and increase organizational agility. By fostering a culture of collaboration between development and operations teams, DevOps enables seamless integration, automated testing, and continuous deployment, thereby minimizing manual interventions and reducing timeto-market. Continuous Delivery complements this by ensuring that software changes can be reliably released at any time through automated pipelines. In 2023, numerous studies have underscored the transformative impact of DevOps and CD in diverse sectors, including finance, healthcare, and cloud-native application development. Recent research highlights include enhanced deployment frequency, improved system reliability, and faster recovery times attributed to DevOps adoption. Moreover, innovations in Infrastructure as Code (IaC), container orchestration, and monitoring tools have further strengthened CD pipelines, enabling teams to scale agile practices effectively. Case studies show that organizations implementing DevOps achieve up to 60% faster release cycles and 40% reduction in failure rates. This paper conducts a comprehensive analysis of 2023 literature, investigating the mechanisms through which DevOps and CD enhance agility. We explore architectural models, tooling ecosystems, and organizational practices that contribute to continuous integration and delivery success. Our research methodology incorporates systematic literature review and multiple case studies. The findings emphasize the importance of automation, culture, and metrics-driven feedback loops. Challenges such as toolchain complexity and cultural resistance are also discussed. In conclusion, we outline future research directions, including AI-driven pipeline optimization and DevSecOps integration, which promise to further streamline software delivery and secure development lifecycles.
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