Optimizing Continuous Integration and Continuous Deployment (CI/CD) Pipelines: Strategies, Tools, and Performance Metrics

Authors

  • Ramadevi Sannapureddy Sikkim-Manipal University of Health, Medical and Technological Sciences, India. Author
  • Sanketh Nelavelli Independent Researcher, USA. Author
  • Venkata Krishna Reddy Kovvuri Keen Info Tek Inc, USA. Author

DOI:

https://doi.org/10.63282/3050-9246.IJETCSIT-V5I1P116

Keywords:

Continuous Integration (CI), Continuous Deployment (CD), CI/CD pipelines, DevOps, DevSecOps, Pipeline optimization, Build automation, Deployment automation, Automated testing, Test automation frameworks, Infrastructure as Code (IaC), Configuration management, Version control systems, Git workflows, Branching strategies, Trunk-based development, Microservices architecture, Containerization, Docker, Kubernetes

Abstract

Continuous Integration and Continuous Deployment (CI/CD) have become central practices in modern software engineering, enhancing development velocity, reliability, and scalability. However, optimizing CI/CD pipelines to minimize latency, reduce resource usage, and improve deployment stability remains a critical research challenge. This study examines optimization techniques, tools, and architectural patterns for CI/CD systems, drawing upon literature from 2015–2021. Through comparative analysis of major CI/CD tools (Jenkins, GitLab CI, Travis CI, CircleCI), the paper explores methods to improve build efficiency, testing automation, and deployment workflows. Results suggest that pipeline optimization depends on three core factors: automation maturity, infrastructure scalability, and feedback loop efficiency. The paper concludes with recommendations for performance tuning and integrating machine learning-based optimization within CI/CD environments.

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Published

2024-03-30

Issue

Section

Articles

How to Cite

1.
Sannapureddy R, Nelavelli S, Reddy Kovvuri VK. Optimizing Continuous Integration and Continuous Deployment (CI/CD) Pipelines: Strategies, Tools, and Performance Metrics. IJETCSIT [Internet]. 2024 Mar. 30 [cited 2026 Mar. 6];5(1):148-60. Available from: https://ijetcsit.org/index.php/ijetcsit/article/view/605

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