Continuous validation is the process where newly written code is automatically integrating into the main database. Instead of a slow and outdated way of integrating every single change separately at the end of a development cycle, continuous integration will allow developers to test their code much easier and multiple times if needed.
This is certainly more than enough reason for automatic validation to be applied by development teams, as it makes it easier for their members to quickly and effortlessly verify the correctness of the code. Automation itself indicates and identifies problems and bugs that need to be fixed. In addition to the aforementioned advantages of speed and ease compared to the old manual verification method, automation also protects the existing main database from new bugs that could arise from the implementation of newly written code. The new code cannot destroy the functionality of the old code. However, the fact that could deter some from building automation is probably that building successful automation takes a lot of time.
The next link in the chain of automation is continuous performance validation. It involves parallel software testing methodologies in which the internal structure and design of an item are being tested. In other words, white and black box testing focuses on all those areas in the code where bugs and problems have occurred to help and assist the development team in removing them as quickly and painlessly as possible.
Therefore, the goal of continuous integration is to seamlessly integrate the old code with the new code. If set up on a sound basis, integration reduces costs and allows testing teams to respond quickly to bugs and code issues.
Continuous delivery is the last extended link that starts where continuous integration ends. The main task of continuous delivery is to automatically and effortlessly transfer code or application to precisely specific infrastructure such as development, production, and testing environments. In fact, most developer teams work in multiple of these environments, and continuous delivery allows them to swiftly make code changes.
It is worth mentioning that the high quality of the code is not in question, since the integration has already been done before the delivery.
The end goal is to deliver quality applications to users.
To do so both CI and CD require continuous testing and continuous monitoring. These various types of tests are performed in a CI/CD pipeline and they include a set of automated regression and performance tests.
The best way to apply this type of test is to demand from developers to constantly run checks in their environments. Such a practice ensures that developers apply their existing code only if it has passed all the parameters.
Regression tests are only the surface of all continuous tests. Security testing, API testing, and performance testing can also be automated.
On the other hand, testing machine learning is more complicated than testing other software systems. You would need data validation for machine learning, more sophisticated CD pipelines include techniques for monitoring ML models, model validation, and model quality evaluation. For a reliable update of the pipelines in production, you need a robust automated CI/CD for machine learning.
In the end, the typical delivery workflow looks like this:
Developers → Code → Supply Chain Management (SCM) → CI/CD server → Production
Continuous Integration & Continuous validation are necessary for companies that want to reconcile the requirements and interests of their development and operation teams. On the one hand, the developers want to implement newly written code as frequently as possible, and on the other hand, the operation teams want a safe and stable application. Thanks to CI / CD, both are getting exactly that. For Developers, it enables more frequent code deployments and for operational teams, environment variables are separated from the application which leads to greater stability.