In an increasingly competitive market, there is a rise in demand to release software faster to meet the customer requirements, without any compromise in the end product’s quality. This puts an additional load on the organizations to develop and test faster for quick releases. Continuous testing is an end-to-end testing process that speeds up the CI/CD pipeline, by incorporating automated processes and tools for testing early and testing often at all points of time. Test automation is an integral part of Continuous testing.
Test automation is a technique to automate predefined repetitive testing tasks, using various test automation tools and testing scripts.
Test automation has marked benefits in terms of accuracy, scalability, dependability, enhanced test coverage, time and effort saving. But is it enough? Test automation eased the testing load, but it could not “think”. Augmenting test automation with the capabilities of AI introduced the dimensions of continuous learning, analysis, and decision making to the continuous testing process by emulating human behavior without any actual human involvement.
As per the recent study conducted by Gartner Inc., the business value of AI will reach $5.1 billion by 2025. In another study conducted by PwC, AI could contribute up to $15.7 trillion to the global economy by 2030.
Let us explore how embracing AI test automation improves the QA Ops process.
How AI enhances test automation
AI test automation, aka intelligent test automation, can spot anomalies, learn from patterns, analyze the data, and then if required, can update the test scripts to reflect the intended changes. This section explains how it does all this and takes testing to the next level.
Testing basics
Getting the basics right is a good start for the testing process. Test data generation and test case generation is an important task and needs to be done with utmost care.
Understanding, analyzing, and then translating the requirements to test cases is a time-consuming job. AI-based tools can do it for you in less time and you can redirect your effort for other tasks.
With continuous testing, the amount of data generated, aggregated over multiple cycles, is huge. Sifting through that data, analyzing the patterns and trends to act as feedback for the next cycle is a herculean task. Also, the input data and test cases need to be updated with every cycle and have to be in sync with the requirements. Using AI/ML shares the load and does the maximum job by generating test data and test cases by learning from previous data/reports and incorporating the new requirements.
Test execution
Test smart – It is not feasible to execute the whole test suite for the smallest of the changes made in the application under test. A smart strategy would be to identify the test cases that are directly impacted by the change and execute them. But with Continuous integration and testing, it becomes increasingly challenging to do that. Intelligent test automation comes to the rescue by analyzing the data from previous test cycles and identifying the right test cases to be executed for the changes done. This saves significant time and effort.
Test right – False failures are the bane of test automation. These are the scenarios when the test automation tool ends up marking a true pass case as a failure. Do take out some time to read our blog “Test automation challenges – False failures” to understand this better.
False failures can lead to unnecessary delays in the schedule because every failed test case needs to be triaged and based on its priority needs to be addressed accordingly. The issue of false failures can be addressed by applying AI/ML algorithms to test automation. The analytical capabilities of AI ensure that the test cases are marked correctly as true pass or true fail by learning from patterns of test results of previous test cycles and new information for the current cycle. Read for more : Test Automation with AI
Webomates has integrated solution Webomates CQ which helps companies to test the Mobile app properly and with effectiveness
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