CI/CD (Continuous Integration and Delivery) is a defined strategy in software development whereby developers are allowed to work collaboratively on a shared code repository. When corrected, an automated build process is performed to detect bugs in the code. The team needs to perform high-quality automated testing where the pipeline provides feedback.
Shift left
The best option for testers is to perform a “shift left” – meaning that testing occurs in the early stages of software development: the design stage and unit testing, providing quick feedback. This verification is fast, and there is no need to integrate with other pieces of software. A strong base of unit checks is essential because they dash as the code builds in the pipeline.
Preparatory stage
In addition to the need for unit testing, you will need a qualified specialist with practical experience analyzing test results. For effective unit testing, one must understand how specific functions behave and interact. The standalone correct operation of a part does not guarantee that it will also qualitatively interact with various dependencies. Therefore, the tester, already at the early stages of development, may need to correct functionality within a set of automated tests, all this can be found on the app testing company site https://testfort.com/automated-testing.
The main problem is that the large-scale integrated system team validates after the code has been deployed to the large environment. It wastes time because the testers must schedule automated tests before the developers write the code.
It does not mean that manual testing should be ignored. However, automation of integration testing provides an opportunity to optimize the process. These tests can then be added to source pipelines and improve the efficiency of the developed software by providing quick feedback to the development team.
Testing areas
Automating all checks is impossible, so knowing which cases need to be unit-tested or integrated-tested is essential. It is necessary to avoid duplication in checks.
Areas for Unit Testing
When the creation of a pipeline begins, unit testing should be referred to as the CI section of the channel because they are evaluated during the code build process. Unit testing includes the following checks:
- Place of entry and exit – the code receives input data and then produces output data, so you need to check everything that is skipped by the code; this will reduce the failures that occur during integration;
- Autonomous options that perform work within the system;
- Marginal values, since the code behaves when receiving arguments, regardless of the source of their input;
- Simple permutations of data (input and output data are understandable);
- Performance and security – Although they are also subjected to load integration testing, verification can be done at the module level to confirm no weaknesses.
After unit checks, automated integration testing is carried out.
Areas to automate integration
These checks are done after the code has been deployed to a large environment. Those areas that are inefficient to check by unit testing are restricted:
- Positive cases of integration;
- Server area – it is necessary to focus on the internal components of the software;
- Security – Unit testing does not exclude automated testing, which guarantees high security and confidentiality.
These are required areas for automated verification to improve the efficiency of the developed products.
Which automated checks are not recommended to be included in CI/CD pipelines?
In the automated testing process, you need to understand which checks do not need to be included in pipelines but detach them from the CI/CD processes and perform daily testing, not in the form of code delivery. Here is a list of these checks:
- End-to-end testing with increased data requirements;
- Visual regression of the user interface;
- Mutation checks;
- Stress testing.
All of these checks should be autonomous for efficiency. Therefore, you should refrain from subjecting the system to these checks in the pipeline.
Aspects of Effective Testing
For testing to be as effective as possible, you must follow the recommendations below:
- Name checks. The names of checks may not always correspond precisely to their functionality, but they reveal the meaning of testing.
- Organization of testing. The most crucial point in writing a test is its readability. It must be ordered by action for a complete understanding.
- Create minimally passable checks. It is recommended not to use complex encryption methods. Often, checks include more information than is necessary for the passability of the review. It complicates the situation.
- Do not use logic in checks. The presence of logic entails errors; therefore, the probability of incorrect test results increases.
- Apply layouts. It allows you to get more accurate test results, will allow you to adjust the effort in the checks, and will improve the coating quality.
- Don’t use multiple statements. They will still be executed if you apply multiple assertions in a test case. Often, such testing ends without success, ending at the first assertion, and the rest of the checks still need to be fulfilled.
- Maintain and update your code. To ensure that the software works effectively and correctly, it is necessary to check the code’s functionality systematically and, if necessary, update and maintain it.
Therefore, automation testing ROI can function well in the CI/CD pipeline, providing a high degree of regression coverage. But for effective results, well-designed tests are necessary.
In our relentless pursuit of efficiency through CI/CD (Continuous Integration and Delivery), a philosophical question emerges: Is the quest for automation in testing a pursuit of efficiency, or does it risk losing the nuanced understanding that only manual testing can provide?
While automation undoubtedly plays a pivotal role in streamlining the software development pipeline, it’s essential to consider all the intricacies of testing. Automated tests excel at repetitive, well-defined tasks, catching regressions and ensuring code stability. Still, it raises questions about whether we can entirely replace the keen judgment and adaptability of human testers. The delicate balance between automation and manual testing challenges us to ponder the nature of true software quality.
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