Just as how software development continues to evolve for the good with new technologies that enter the market, all attributes of product and engineering need to evolve too – whether it to be design, architecture, marketing and communications, product management, quality, operations and support etc.
In the recent times, Artificial Intelligence is one such powerful technology that has been giving a huge facelift to several disciplines including quality. Let’s take a closer look on its influence specific to quality and software testing.
A few years back we had done a webinar for EuroStar on Augmented Reality, covering both how to test for AR applications and also leverage AR to get smarter with the overall testing process.
This was very well received and the same message continues with every new technology that comes in – explore how to test applications developed with them while also looking at how to leverage them to make internal processes more effective.
Artificial Intelligence at the core is nothing but very smart automation. The tester creates the algorithms to power up systems to work with defined intelligence, rather than the intelligence in real time coming in from a physical tester.
Isn’t this what automation achieves too?
However, automation now done leveraging artificial intelligence is much more powerful and effective, with ability to touch newer horizons, thanks to the several other developments that have happened alongside.
For example, with minimal intervention from testers, systems are able to learn and unlearn practices, read through large volumes of data, recognize images, process speech etc. all of which help not just developers but testers too.
To give specific examples, accessibility testing has been one area that has had minimal automated intervention thus far, given the need for cognitive skills and real user experiences. Thanks to AI-based testing approach, an automated app testing strategy is slowly becoming possible.
For example, leveraging natural language processing, and image recognition solutions such as Google Vision API, Image AI, alt text for images, are more accurate than in the past, not only making them reliable but also much faster.
This now gives no reason for both developers and testers to miss alt text in their efforts to digital accessibility and inclusiveness.
At QA InfoTech we have also developed a solution towards checking for keyboard accessibility of all interactive elements using machine language algorithms.
If AI can help to such an extent in an area that has traditionally remained manual, one can only imagine the scale and potential it holds in other areas which may be more black and white.
Also, AI can help not just with test execution, but even with putting together test strategies and plans, learning from past efforts, automating result logging, generating reports and dashboards, bringing in predictive analytics to the testing efforts, assimilating data from disparate sources, especially given how connected solutions have become in the mobile world.
It is able to easily bring together mobile app testing, associated mobile testing services with other non-mobile solutions, making test efforts more optimal, fast and reliable. In all, it is able to bring in both efficiencies and cost savings further enabling testers focus on the more strategic pieces and move left in the larger scheme of software engineering.
If this is the potential today, this will only continue to scale in the coming years, as newer enablers make the core AI phenomenon even more powerful.
Connect with experts at QA Infotech to find out how we can help you to take your organisation to the next level.