The problem of deciding between using manual test processes and automated testing is real. There are dozens of reasons why QA analysts and software testers should migrate to automating their testing processes. Reliable insights and test reports are the main reason why automated testing is superior to manual processes.
Automated testing provides reliable test results since it removes the possibility of human error during the process. At the same time, automating test processes improve productivity and optimizes revenue generation. Undoubtedly, test automation trumps manual testing by a long margin. Automated testing is best-suited for regression testing, load testing, performance testing, and high-risk functional test cases. Because of this, in every DevOpscycle, automated testing is an integral part. In short, you should automate every test that you can as manual testing is good only when a test is to be run once or twice. So what are the different automated test processes and tools? Let’s find out more about the different types of automated testing to choose the perfect one for the best results.
Regression testing is the most tedious test process since it is repetitive and consumes a lot of time. The repetitive nature of these tests makes them perfectly suitable for automated processes. Running regression tests on software projects reduces the time QA analysts and software testers spend running the same tests.
Whenever a new feature, unit, component, or codebase has been added, automated regression testing significantly reduces human intervention, which contributes to productive software development processes. Having scripts or tools that conduct regression tests is the fundamental building block of automating and optimizing the entire testing phase in software development.
Automated Unit Testing
Unit tests are generally done by software developers, especially when working in a DevOps team. This software testing is done before system units are integrated to make a fully functional product. Before integrating the unit with other system units, software developers run tests on its functionality.
During the process, software developers can fix bugs in the code or other errors that could impact integration and overall functionality. Automating unit testing streamlines the workflow and minimizes potential problems that could be faced when running integration tests. Developers can write scripts or use comprehensive automated testing tools to automate all software unit tests.
After the individual software units have been integrated and compiled into the development project, integration tests are conducted. The core purpose of integration tests is to ensure that the different software units interact well with each other. When the different units are being developed by different people, integration tests are very critical.
This type of test is done after conducting unit testing and validating that individual software units are operational independently. Automating integration testing can help expedite the process of validating whether the individual units can function collectively. Integration tests are conducted using test cases that improve the design and implementation flexibility of the individual software units.
Build Verification Testing
Build verification testing, also known as smoke testing, improves the overall quality of a software product under development. Implementing build verification test processes allows software teams to objectively ascertain if they are reaching the KPIs of the project.
Through smoke testing, QA analysts and testers can test the viability of the project. This is achieved by running tests on key functionalities. If they report negative results, the project is returned to the development phase. The testing process also includes verifying functional stability. If the key features are not functional or aren’t stable enough, this also warrants the project to be sent back to development.
Non-Functional Test Processes
Another critical software testing process is non-functional tests, which primarily focus on performance and system reliability. The tests conducted during this phase are focused on ensuring that validated and integrated software units can perform well under high-stress environments. Non-functional tests differ from other testing processes since they monitor the rate at which the system can function.
That is contrary to other test processes that solely conduct tests focused on functionality and system behavior. There are several ways to automate non-functional test processes, including tools that replicate high traffic volumes. Automating non-functional testing proves that the software can perform well even when facing large volumes of traffic and lots of data queries coming in simultaneously.
Automating API Tests
Software projects are incomplete without APIs that handle data requests from databases and integrate different systems functionally. Subsequently, a different type of test has been dedicated to inspecting API functionality and integration. Every API must be functional to integrate backend systems with the front-end user interface.
Once the user interface is operational and backend systems have been integrated, the next step is testing APIs. This process can be very tedious and time-consuming and thus needs to be automated. If aspects of the software units change, it is critical to ensure that the APIs still function properly. Therefore, this test is generally done repetitively, so writing testing scripts can make it convenient.
AI-Powered Security Testing
Before deploying software products, ensuring user and business data security is paramount. Without strong security measures, the company could negligently lose operationally critical insights. To prevent this, the security of the software product must be comprehensively tested before deployment.
Using AI-powered tools automates the process and covers a wider range of potential security defects. AI-powered security testing tools can uncover weak points that could allow a breach using the latest exploits used by cybercriminals. Manual security tests might not be as accurate and take longer to complete.
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