Can AI Power Up Your App Testing Efforts?
Can AI Power Up Your App Testing Efforts?
Let's look at whether or not AI can power up your app testing efforts as well as key reasons for considering AI within your software testing strategy.
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Software Testing and Quality Assurance has been leveraged to bring speed and accuracy for the Digital Transformation efforts by enterprises. Over the last few years, Test Automation has been increasingly leveraged to ensure optimal accuracy for various digital initiatives. In the current scenario, software development teams are adopting Artificial Intelligence (AI) to execute testing tasks that are repetitive and time-consuming. The underlying purpose is to not only bring speed, but also ensure accuracy while processing massive chunks of data to derive meaningful inferences.
According to a PWC research, "45% of total economic gains by 2030 will come from product enhancements, stimulating consumer demand. This is because AI will drive greater product variety, with increased personalisation, attractiveness and affordability over time." AI is indisputably creating a positive stir across various sectors, and when it's about application testing, the role is equally critical.
AI for Application Testing — What's the rush?
The application surface is getting complex each day and various applications interact with each other through APIs, which add up to the complexity. Apart from increasing the complexity, there is an immense rush to bring the application to the market. With a demanding market scenario, the releases that would happen over a month's span are being done within a week's time. This is putting a tremendous load on testing. Hence, Machine-based intelligence is needed to overcome the testing and QA challenges that testers would face on a regular and recurring basis.
Constant feedback and scrupulous tracking is needed to ensure transparency and evaluate the progress in the testing and development cycle. AI tools come with inbuilt capabilities to work smarter and track every inch of activity. This helps to streamline software testing and make it more effective for teams. AI-powered continuous testing platforms are able to track, test, and constantly update algorithms. This enables teams to track even the slightest change in the testing cycle.
AI platforms are being widely used for object application categorizations for all user interfaces. Additionally, it is even possible to customize the controls and eventually create a technical map with labels for various controls that must be used. It is expected that AI will also build capabilities to analyze and assess user behavior, where a risk preference can even be assigned to identify the gaps within the application. It will help to identify the bottlenecks and then determine the tests that must be undertaken.
AI will help testers to make data-driven decisions and take a risk-based automation approach. In fact, by getting AI within the test strategy, testing teams can skip the need to update their test cases manually and identify gaps in a much more effective way.
Key Reasons for Considering AI Within Your Software Testing Strategy
We have very much established the rush for incorporating AI capabilities within your testing agenda and strategy. But, if we have to list down the key reasons for considering AI, these could be the apparent ones.
Accuracy and Speed are two established reasons for leveraging AI for App Testing efforts. AI is expected to empower both the Testing and Development teams. The greatest benefit of all is that it enables developers to access the shared automated tests and conduct tests initially before the application goes into the hands of the QA folks. Hence, major bottlenecks and gaps are resolved in the initial screening of tests itself. This helps to substantially save time both at the testing and development end. It further validates the results that have been derived by both the teams.
Test Thoroughly, Don't Miss Out
While conceptualizing any software testing strategy, it is important to ensure that you are able to maximize test coverage for your application. Automated testing helps to expand the scope of testing for your application and look at all the aspects within it. When AI platforms/tools are leveraged to execute automation tests, testers and developers can multiply execution of diverse test cases. Ultimately, it helps to maximize test coverage that is practically impossible with manual tests.
The Application Was Expected Yesterday!
There is no limit to the kind of pressure that development and testing teams face to get the application faster to the market. Manual Testing can hardly cope up with these pressures. Hence, automation with machine-enabled tools is critical. There are automated tests that get executed on a recurring basis to confirm a particular outcome. This can be done at a lower cost with a one-time investment. Ultimately, the testing cycle is reduced, which translates into faster turnaround and time-to-market.
A research by Forrester states that testing is currently the "most popular phase of the software delivery lifecycle in which to apply AI."
Organizations are considering AI to accelerate test automation efforts and even look at smarter methodologies to make the testing activity more and more cost-effective. QA plays a major role in ensuring consumer experience, where test automation is very much necessary. AI can bring in the ease of automation and execution in instances where the performance and functionality needs to be rigorously tested. Robotics and artificial intelligence platforms are gradually taking over the software testing activities, as they are easy to use, implement, and cost as well as time effective.
Published at DZone with permission of Hiren Tanna , DZone MVB. See the original article here.
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