It seems like everything around us is coming to life and becoming intelligent. Tech companies know that increased automation is the way of the future. Advanced technology like artificial intelligence (AI) and machine learning (ML) is fuelling the most exciting innovations in recent history — think self-driving cars, virtual and augmented reality, automated investing, improved medical imaging, and more. The benefits of this technology are becoming more and more obvious, and companies are rushing toward adoption and racing to build it into their products.
Before diving into the approach for testing smart products, let’s differentiate between artificial intelligence and machine learning. Though the terms are often used interchangeably, there are some key differences.
Whether it be the health, finance, or entertainment sector – every industry is trying to innovate and use AI-based apps that help automate tasks. This makes testing the apps for automation a business-critical activity. However, there are multiple testing related challenges that organisations may face while leveraging AI for testing apps for quality, such as:
Today’s IT teams have a great selection of application testing frameworks to help create functional test cases, but what we are missing are tools making the creation of test suites easier and faster. We need solutions that will know how to execute smart tests on different applications against real devices with no need to manually show how and maintain the once created tests. We all still remember those record and replay tools for testing from some decades ago…
Luckily there is a new computer science field that is addressing exactly this problem. Machine learning (ML) and Artificial Intelligence (AI) are what many predict the solvers of this problem. There are two ways to tackle this, either you create your own specific AI that will know how to test your application in the best possible way or, you take an existing AI general solution that will do advanced exploratory testing on all kinds of applications, including mobile games.
Below are the four key elements that we should consider while testing AI/ML applications:
Artificial Intelligence (AI) and Machine Learning (ML) are transforming the technology landscape of the digital era. New-edge technologies require a new approach to software testing, one that reduces risks while improving the overall experience.
Using AI and ML, below tools will help unlock the power of data (such as test artifacts, project documentation, test results, defect logs, production incidents, etc.) and drives innovation, accelerating QA efficiencies beyond the reach of traditional QA practices.
If you feel overwhelmed with staying up to date with all the latest testing tools and best practices, stay Tuned. ☺