Today, testers require to be equipped with more advanced techniques to assure the quality of the software published at the speed of Agile/Continuous Delivery. Automation Testing is undoubtedly the best method for testing in a continuous delivery cycle.
Every business will have an online presence and fast-changing employment requirements to handle dynamic business requirements. Matching the changing application needs in the increasing number of devices in a short time practicing Automation Testing with an acceptable test coverage is not unrealizable but doubtful.
With little human input, the AI is required to help testers analyze and revamp the automated testing process(not just test development). AI allows the tests to be automated with better efficiency and precision. Essentially put, AI is intelligent automation.
AI encourages automation, works faster when trained to identify errors, their causes, and suggest fixes, and make a connection of a collection of related tests. This not only makes test automation faster but also more accurate.
AI should be proficient in automatically accessing data, running tests, and being able to recognize an error and also identify other suitable affected tests. This approach enhances the quality of the tests.
Driving agility and quality with Intelligent Test Automation
In an effort to expedite digital transformation, organizations are embracing the modern software development methods of DevOps and Quality Engineering, while turning Quality Assurance to the left. With the help of test automation, it has been feasible to perform software testing of a code in correspondence to the development, since the commencement of an SDLC, while allowing a continuous feedback pipeline to encourage early and continuous improvement.
However, normally, the test automation cases are high maintenance and not reusable. This makes test automation an expensive affair for the organization and they gradually seep back into the standard testing practices. With Intelligent Test Automation, the software testing strategy includes a model-based testing approach. In model-based testing, a TDD/BDD way is followed and the test cases are generated and managed automatically.
Having a model-based testing strategy enables organizations to implement an end-to-end testing practice across all enterprise methods. This results in higher test coverage, the creation of efficient test cases, and lower maintenance costs. The smart algorithms managed by Artificial Intelligence and Machine Learning technologies combine the analytical feature inside the software testing lifecycle. This suggests that the outcomes can automatically be estimated within an Intelligent Test Automation scenario, which further decreases the involvement of manual resources.
Advantages of AI in Automation Testing
- No unattended errors. The testers can take a back seat and allow AI to perform the tests with less or no interference. As soon as a bug is submitted, the AI alerts the inspector of the error, why it failed, and what could be a possible fix for it, and doing quick fixes too.
- Improved quality. AI not only makes testing quicker but also enhances the quality by processing huge amounts of data at a time to recognize similar error trends and recognizing anomalies.
- As the AI testing method is automated, the software developers and testers will take a quick feedback report on the functioning and the performance of the applications. Also, the bugs will quickly be fixed and hence, the products can be launched faster into the market.
- Effective Automation Testing. AI in automated testing can improve the overall depth and range of tests resulting in the overall improvement of software quality. Automated software testing can examine data sets, locator values, repositories, internal program states, in order to decide if the software is behaving as required.
- AI can help you correct the set of tests to be run for the application modifications to provide good test coverage with optimized testing efforts that are not feasible with just Automation Testing.
- AI-driven test automation can handle repetitive tasks to satisfy the continuous delivery requirements for increased productivity.
This process of writing tests first also with the shift-left strategy has become popularized as a good habit for automation testers. However, it is sometimes not executed at all. With artificial intelligence’s solutions, this would be easier. Businesses executing AI at the enterprise level are already enduring greater operational efficiency and better productivity outcomes.
Automation gives businesses an opportunity to replace mundane, repeated processes. Automation combined with collecting input, analyzing data, efficiency-finding, and even making decisions of AI will create a great pact for the future and it is now with the new age of smart automation! And, with the timely engagement of testers to verify their actions, quality is assured.
TestUnity’s Test Case Management Strategy provides AI algorithms for Test suite optimization and allows to predict the next for test suite queuing. Schedule a discussion and consult with our test automation experts.
Testunity is a SaaS-based technology platform driven by a vast community of testers & QAs spread around the world, powered by technology & testing experts to create the dedicated testing hub. Which is capable of providing almost all kind of testing services for almost all the platforms exists in software word.