Copado has acquired Qentinel as part of an effort to incorporate AI-infused application testing within a DevOps platform and build applications using low-code tools across multiple software-as-a-service (SaaS) platforms.
The Copado continuous integration/continuous delivery (CI/CD) platform was originally created to accelerate the development of low-code applications on top of the Salesforce platform. Now, however, Copado is expanding the scope of its DevOps reach to include other SaaS platforms.
Andrew Leigh, chief marketing officer for Copado, said as organizations increase the number of SaaS platforms they employ in the wake of the COVID-19 pandemic, it quickly became apparent there would soon be a need to build applications that enable organizations to unify business processes spanning multiple SaaS applications.
As part of that effort, Copado’s acquisition of Qentinel aims to advance that goal by making it simpler for both developers and end users to participate in the application testing process, Leigh said.
Qentinel is accessed mainly via the cloud to automate the creation of application tests. Rechristened Copado Robotic Testing, the core of the Qentinel platform is based on the open source Robot Framework that understands scripts, executes them and generates logs and reports. A set of built-in libraries comes packaged with Robot Framework and addresses a wide range of testing scenarios. Qentinel customers include Brunswick, Elisa and Konecranes.
Gartner is forecasting that, by 2024, 75% of large enterprises will be using AI-enabled test automation tools to advance continuous testing across different stages of the DevOps life cycle. Much of the interest in AI-infused application testing tools is being driven by necessity as the pace of low-code application development exceeds the pace at which manual testing processes can work. As application testing becomes a bottleneck, organizations will naturally look toward automation platforms that employ machine learning algorithms capable of testing applications at scale.
It’s not clear to what degree those platforms might eliminate the need for humans to test applications. The expectation is humans will be able to test application functionality at a deeper level versus, for example, merely checking to see if compliance mandates have been met. Many smaller organizations building low-code applications don’t have any meaningful application testing capabilities at all. Testing platforms infused with AI will prove to be especially crucial for citizen developers that require guidance to build a secure application that can actually scale, noted Leigh. The more SaaS application environments those applications span, the more critical testing becomes, added Leigh.
One way or another AI is coming to application testing. After all, machine learning algorithms don’t ever need to take a day off or suddenly leave to take a better offer elsewhere. In fact, soon, most organizations soon won’t even consider a test automation platform that doesn’t have AI capabilities as they attempt to reduce the level of manual, monotonous and repetitive testing tasks. It may take a while for everyone to gain confidence in the algorithms, but it’s now more a question of the degree to which AI will be employed rather than if.
The data used to train AI models needs to reflect the production environments where applications are deployed.
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