Automated Testing in 2021: A Look at the Changing Landscape of Automated Software Testing
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What is Automated Testing?
Though a critical step in the DevOps lifecycle testing can be painfully time consuming, inefficient and monotonous. As a result, developers are increasingly automating the testing process to accelerate speed of delivery as well as reallocate focus towards more high value activities.
Using an automated tool such as Avo and EdgeVerve to execute test case suites and predefined actions on software applications, Afterwards, it compares actual outcomes to expected results and, using that information, generates detailed test reports. In addition, not only can testing automation run 24/7 over and over again on multiple platforms, it also allows testers to reuse test scripts and maximizes scalability.
Though manual testing may still be appropriate in some instances, automated testing enables continuous integration and delivery (CI/CD), an agile development methodology designed to produce a constant flow of software updates into production to quicken release cycles, lower costs, and reduce the risks associated with development.
There are numerous different approaches to software testing. For example, some testers use the “testing pyramid” approach, a framework that breaks down the testing process into 3 layers:
- Unit - testing individual units of code or groups of units
- API/ Integration/ Component Tests - testing how units of code interact with one another as well as the larger IT ecosystem
- End-to-end - testing the full, end-to-end process across multiple systems
Other types of testing include:
- Functional testing - ensures the software meets its predetermined requirements and works as intended from the end-user’s perspective.
- Regression testing - “repeated functional testing,” ensures that old features of the software continue to work as intended after new ones are added.
- API testing - testing the application programming interfaces (APIs) that are exposed by the developers to third-party developers. It is done to determine if the APIs meet the expectations for functionality, reliability, performance, and security.
Testing Automation VS. RPA
Testing automation and RPA are remarkably similar from a technological standpoint and can sometimes be confused. RPA (Robotic Process Automation) uses scripts or “bots” to automate repeatable, rules-based processes. Unlike traditional software solutions that require programming, RPA bots observe digital behavior, learn from experience and mimic human actions.
RPA can be applied to a wide variety of business and IT processes including software testing. However, automated testing tools can only be applied to a software product and cannot perform general business processes.
The Rise of AI and ML-Powered Testing
For example, AI can script 100 tests in 1/100th of the time a single person could. Using a technique known as spidering, advanced ML algorithms can crawl an application, collecting important data about the features being used in the application (e.g. recording videos of test scenarios, taking screenshots, measuring execution time, etc.). The testing tool can then compare the current working pattern with the data which is a part of the ML model. Any deviation (e.g. slower speed of execution, issues with implementation, problems with GUI/UI) are flagged as potential problems in the final report.
According to Perfecto,the incorporation of AI and ML into these tools has also evolved traditional software testing practices into 4 new approaches:
- Differential testing — comparing application versions overbuilds, classifying the differences, and learning from feedback on the classification.
- Visual testing — leveraging image-based learning and screen comparisons to test the look and feel of an application.
- Declarative testing — specifying the intent of a test in a natural or domain-specific language, and having the system figure out how to carry out the test.
- Self-healing automation — auto-correcting element selection in tests when the UI changes. Eventually natural-language processing (NLP)-based automation will enable scriptless testing tools.