Efficient CI/CD Strategies: Integrating Git with automated testing and deployment
Sr Cybersecurity Analyst, Department of Cybersecurity, Financial Institute, Lapalma, CA, USA.
Review Article
World Journal of Advanced Research and Reviews, 2023, 20(02), 1517-1530
Publication history:
Received on 20 September 2023; revised on 26 November 2023; accepted on 29 November 2023
Abstract:
Continuous Integration( CI) and Continuous Deployment (CD) allow prompt delivery of quality software with lesser disruption between development teams. With this simple yet effective Process, Git is a superb tool; the utility combination of Git with some YAML CI/CD templates makes us a better CI/CD pipeline. Keywords: Continuous integration and delivery, Continuous integration and delivery with Git, Automated testing and deployment, Lean continuous integration and delivery, Agile software development 1. Introduction Continuous Integration (CI) and Continuous Delivery (CD) are two of the most vital methodologies in today's software development environment, as they aid developers in developing, testing, and releasing software in much shorter cycles. This study examines an overview of lean CI/CD with Git - automated testing and deployment methods and techniques and gives an extensive overview of approaches and methods providing tools for lean CI/CD sessions.
When Git is embedded in CI/CD workflows, code can seamlessly be transferred between development, test, and production environments to give immediate feedback and reduce downtime. These tools simplify the automatic merging of code, running of tests and deployment, thereby reducing human mistakes, increasing productivity, and speeding up a the process of delivering a higher quality software. CI/CD Tools (Jenkins, GitHub Actions, GitLab CI, etc) are there to automate these processes and help out with the scale.
CI/CD involves automated testing to make sure you have sufficient quality and reliability in your software. These tests include unit tests, integration tests, and end-to-end tests, which help developers spot and address issues earlier and prevent defects from escaping from one level of system development to another, making this process less resource-intensive. Moreover, deployment automation (e.g., Docker and Kubernetes) allows organizations to deliver continuously without having to depend too much on people. Then, blue-green and canary deployments allow for updates with zero downtime.
It further delves into the economic and operational impacts of adopting CI/CD pipelines. Hence, time-to-market is reduced, resources are utilized better, and teams collaborate well, etc. Thus, resorts to containerization and orchestration technologies render deployments cars scalable and reliable, even in complex ecosystems. Configuration drift, manual errors, etc, are some of the bottlenecks for maintaining large-scale environments and Infrastructure as Code ( IaC ) tools ( Terraform, AWS CloudFormation, etc.) allow for consistent and repeatable processes for deployment.
Companies who have adopted CI/CD strategies share how it changed their processes via case studies. By integrating Docker-based deployments with GitLab CI/CD, an e-commerce company was able to increase their deployment speed by as much as 40%, while trunk-based development paired with Jenkins allowed a financial services company to increase their test coverage and speed up releases. These are just some of the many very real benefits of incorporating Git, automated testing, and deployment strategies into a CI/CD process.
While they are useful, providing CI/CD pipelines can be complicated. The challenges of scaling pipelines to large repositories, securing secrets and toolchain compatibility are non-trivial. In this paper, we will point out these challenges and suggest steps to overcome them. To help organizations get the most out of CI/CD in their workflows, best practices such as enforcing consistent branching policies in CI/CD tools, integrating quality gates (like SonarQube), and using comprehensive monitoring and logging systems are also covered.
Looking forward, new trends, including AI-based CI/CD, serverless pipelines and GitOps, will transform the software delivery landscape. The AI-powered predictive analytics will be used to call relevant offerings from the productivity pipeline, and the serverless architectures will reduce the infrastructure load to run the machine-learned tasks. Taking this a step further is GitOps, a newer paradigm centred around declarative infrastructure management, which can help simplify complex deployments.
Keywords:
Continuous Integration; Continuous Deployment; DevOps; Software Engineering; Application Delivery; Automation
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Copyright © 2023 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0