We’ve all had the experience of opening an app and having a not-so-great user experience – the text doesn’t line up with a button, images run off the page, or the scroll doesn’t work. With applications being table stakes for any significant technology solution, it has never been more critical for organizations to optimize their visual testing process to eliminate or significantly reduce the prevalence and impact of visual bugs.
Visual testing, a quality assurance process designed to ensure a digital product is displaying and functioning the way a designer intended, is a traditionally human process conducted by test automation engineers. Since it is a human process, there is always the risk of human errors or oversight. There is only one chance to make a great first impression with your product. Automated visual testing is the insurance policy for great first impressions, so the process must be dialed in.
Artificial intelligence (AI) in quality control has allowed software to optimize and improve testing in recent years. With artificial intelligence assisting visual field testers, the process evolves from a manual process of looking for inaccuracies visible to the naked eye to a process that will pick up all inaccuracies, even those not visible or recognizable to a human.
Organizations adopting artificial intelligence-based software solutions benefit greatly from their AI visual testing process. What are the benefits that these tools provide?
A given application can have thousands of pages and assets for review and comparison to a designed mockup for discrepancies. After a while, because it is a human process, the reviewer can become tired, bored, or complacent, resulting in missed bugs or skipping reviews. Utilizing an automated visual testing tool like MagnifAI helps highlight all visual discrepancies, even those not visible to the eye. That capability, in turn, reduces the time a development team spends correcting bugs noticed and reported by users after launch. Through automation, those leading the visual testing process can leave repetitive, manual tasks behind and begin to focus on overseeing the entire testing process.
Improved user experience
In research conducted by ACM SIGCHI, responses indicated that 94% of a user’s first impression of a product relates to visual aspects. For example, when digital products such as websites or mobile applications look and work as they should, users feel compelled to increase their time on the given application or, depending on the user’s case, their frequency. User experience can make or break the success of a new application, as poor reviews hurt app store rankings.
Enhanced brand reputation
Providing a great user experience leads to an enhanced brand reputation. Having a reliable and well-executed product enables familiarity and builds trust with users. An AI visual testing process reduces visual errors known to drive customers away. If the user experience of your product is subpar, it will push would-be customers to seek alternatives – likely falling into the hands of your competitors.
Reduced time to market
Traditional visual testing is labor intensive, which translates to time for organizations. In addition to the human time it takes to do the testing, the lost time for not launching the product becomes a factor. Visual testing automation software such as MagnifAI allows visual testing teams to work faster and more frequently, dramatically reducing the time it takes to find and fix bugs. Additionally, fixing visual errors and course correcting for testing mistakes after launch is costly and disruptive to development teams.
Visual testing software, such as MagnifAI from Globant X, is industry agnostic and easy to use. By adopting visual testing software, test automation engineers can work more efficiently and effectively to ship products that provide exceptional customer experiences.