Continuous Integration and deployment testing.
There are two terms to understand here, which form an integrated testing procedure.
Continuous integration (CI) describes the process of automatically building and testing code every time someone makes code changes to version control (the controlled version of a released product). A code is committed to the main or a subsidiary branch of the build system, where it is tested and validated before launch. Doing it this way allows developers to isolate any potential risks before merging with the main code.
Deployment testing refers to a test installation process for developed software, also known as installation testing or implementation testing. The process involves full, partial or upgraded versions that install/uninstall processes integrated into the product. Deployment testing follows product ‘sprints’, small updates to the product that are integrated over time, and tests/updates them as required.
Why would you want continuous integration and deployment testing?
As a project manager, you’ll want to ensure that applications are tested and deployed quickly and reliably, improving software quality and reducing time to market. Without such a system, creating updates, finding errors and integrating new features into a live application is far more difficult and may result in significant downtime.
Such a process reduces testing time, improves software quality, and ultimately delivers high-quality applications more quickly and reliably.
How does continuous integration and deployment testing work?
Continuous integration and deployment testing should aim to complete the following:
- Identify the key components of the application that can be integrated and deployed continuously.
- Set up a testing infrastructure that supports continuous integration and deployment, such as a CI/CD pipeline.
- Establish clear testing procedures and protocols to ensure that applications are tested thoroughly and consistently.
- Use data analytics and machine learning to optimize the testing infrastructure and improve testing efficiency.
- Use cloud-native tools and services to improve the efficiency and scalability of the testing infrastructure.
- Use automation tools to support continuous integration and deployment, including automated testing, builds, and deployment.
- Use feedback and input from stakeholders to improve the testing and deployment process continuously.
- Establish a process for rollbacks and recovery in case of failed deployments.
- Train and educate all stakeholders on the continuous integration and deployment process and best practices.
Continuously monitor and evaluate the effectiveness of the continuous integration and deployment process, and make improvements as needed.
The value of continuous integration and deployment testing
The process allows for gradual and scheduled updates to be integrated and tested whilst in a live application, without the need for downtime. Various product versions are available anytime, switching out as the newer version is cleared for customer use. Such a system has improved safety and fewer opportunities for things to go wrong than a full release at once.
Main advantages of continuous integration and deployment testing
- Enables faster and more frequent software releases
- Facilitates more efficient and effective testing processes
- Improves overall quality and reliability of applications
- Helps prevent performance issues and downtime in production
- Facilitates automation of testing processes and controls
- Enhances collaboration and communication within the testing and development teams.
A common user story
“As a Product Manager, we want to create a continuous integration and deployment process for cloud application testing to ensure that our applications are tested and deployed more quickly and reliably, improving software quality and reducing time to market. By identifying critical components of the application that can be integrated and deployed continuously, setting up a testing infrastructure that supports continuous integration and deployment, establishing clear testing procedures and protocols, and using data analytics and machine learning to optimize the testing infrastructure and improve testing efficiency, we can reduce testing time, enhance the quality of software, and ultimately deliver high-quality applications more quickly and reliably.”
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