11 Best Practices for Continuous Performance Testing

In order to develop a perfect and best product, your software development teams need to execute continuous performance testing as with this they can promptly determine key functional errors as well as those load-handling and performance errors that are hard to detect. If you’re ready to utilize continuous performance testing, knowing these best practices would help you:

Focus on performance SLAs: Introducing codes in an application for the purpose of testing shouldn’t make it fall apart or fall short of the SLAs. What this means is that you should use your performance SLAs as acceptance testing for every iteration. Therefore, changes made to the iteration that affect just a small part of the overall code would be acceptable because performance issues in such a case would be limited to that particular part of the application.

However, for general SLAs that cover the entire application, a broader list of constraints should be added that will be examined for each iteration to verify if it meets a nominal definition of “being completed” without breaking any of the constraints.

Software Developers and testers should work closely: There is strongly need of developers and testers work closely in an agile environment in order to give the best product and deliver results quickly. This is also said that developers need to work like testers when working on the project. And software testers needs to be updated about the development tasks by attending the agile-scrum meetings. This cooperation would assure a smooth and constant software testing process.

Take up a test-driven formulation: If you are planning to work smoothly and in a right manner then you need to give the whole project a head start, product attribute and test scripts must be created simultaneously. This would guarantee that there’s no need for the software testers to vivify or revise the test scripts if some effect are made in the current software.

Develop story/scenario-based test cases: Unlike the waterfall development model, modern development procedure often don’t make elaborate specifications available to users. In such cases, story- or scenario-based tests must be created instead of making tests based on a stiff set of cases. Using such tests, testers can cover a broad range of tests that may not be basically documented.

Using dynamic tests for dynamic environments: In the past, multiple computing script could have been tested using a best test script. Nevertheless, in today’s fast changing environments, the tests too need to be dynamic. And that’s why infrastructure as code approach is becoming popular, where all virtual hardware, computing assets, and applications are represented as software.

Increased use of test automation: In most software testing situations, test automation is still restricted to the primal stages of unit testing. Nevertheless, they need to be applied all through the deployment procedure, especially during the later stages. To make this go on, the best method is to make test scripts that can be enclose into distinct software services/constituent, which can then be used, as and when the need develop in different situations, in a relatively unbelief style.

Increased use of application performance monitoring tools: APM tools are always used as essential constituent of several testing procedure today. Such tools need to be used progressively to continue shift-right performance monitoring.

Use a build server to initiate recurring tests: With every build, performance tests must feature among the recurring tests. You can attain this by initiating the test via the build server, and including the test results produced within the build tool. This way, the person who started the build would be able to see the results, and know the changes that were made in that build. This means fixing performance issues (if any) that come up.

Proper planning for tests for CI, post-sprint and nightly builds: Tests for nightly builds, CI (continuous Integration) builds, and post-sprint builds could differ significantly. And the difference could be between a solitary change made in a day against all the changes made during a sprint. Thus, performance tests for such builds should begin small and use the available internal loads.