Kevin Surace
Chairman & CTO, Appvance.ai
With the need for frequent builds—often many times in a day—QEs can only keep pace through AI-led testing. It is the modern approach that allows quality engineers to create scripts and run tests autonomously to find bugs and provide diagnostic data to get to the root cause.
AI-driven testing means different things to different QA engineers. Some see it as using AI for identifying objects or helping create script-less testing; some consider it as autonomous generation of scripts while others would think in terms of leveraging system data to create scripts which mimic real user activity.
Our research shows that teams who are able to implement what they can in scripts and manual testing have, on average, less than 15% code, page, action, and likely user flow coverage. In essence, even if you have 100% code coverage, you are likely testing less than 15% of what users will do. That in itself is a serious issue.
Starting in 2012, Appvance set out to rethink the concept of QA automation. Today our AIQ Technology combines tens of thousands of hours of test automation machine learning with the deep domain knowledge, the essential business rules, each QE specialist knows about their application. We create an autonomous expert system that spawns multiple instances of itself that swarm over the application testing at the UX and at the API-levels. Along the way these Intelligences write the scripts, hundreds, and thousands of them, that describes their individual journeys through the application.
And why would we need to generate so many tests fully autonomously. Because applications today are 10X the size they were just ten years ago. But your QE team doesn’t have 10X the number of test automation engineers. And because you have 10X less time to do the work than 10 years ago. Just to keep pace with the dev team requires each quality engineer to be 100X more productive than they were 10 years ago.
Something had to change; that something is AI.
AI-testing in two steps
We leveraged AI and witnessed over 90% reduction in human effort to find the same bugs. So how does this work?
It’s really a two-stage process.
First, leveraging key AI capabilities in TestDesigner, Appvance’s codeless test creation system, we make it possible to write scripts faster, identify more resilient accessors, and substantially reduce maintenance of scripts.
With AI alongside you as you implement an automated test case, you get a technology that suggests the most stable accessors and constantly improves and refines them. It also creates “fallback accessors” when tests run and hit an accessor change enabling the script to continue even though changes have been made to the application. And finally, the AI can self-heal scripts which must and update them with new accessors without human assistance. These AI-based, built-in technologies give you the most stable scripts every time with the most robust accessor methodologies and self-healing. Nothing else comes close.
The final two points above deal with autonomous generation of tests. To beat the queue and crush it, you have to get a heavy lift for finding bugs. And as we have learnt, go far beyond the use cases that a business analyst listed. Job one is to find bugs and prioritize them, leveraging AI to generate tests autonomously.
Appvance’s patented AI engine has already been trained with millions of actions. You will teach it the business rules of your application (machine learning). It will then create real user flows, take every possible action, discover every page, fill out every form, get to every state, and validate the most critical outcomes just as you trained it to do. It does all this without writing or recording a single script. We call this is ‘blueprinting’ an application. We do this at every new build. Multiple instances of the AI will spin up, each selecting a unique path through the application, typically finding 1000s or more flows in a matter of minutes. When complete, the AI hands you the results including bugs, all the diagnostic data to help find the root cause, and the reusable test-scripts to repeat the bug. A further turn of the crank can refine these scripts into exact replicas of what production users are doing and apply them to the new build. Any modern approach to continuous testing needs to leverage AI in both helping QA engineers create scripts as well as autonomously create tests so that both parts work together to find bugs and provide data to get to the root cause. That AI driven future is available today from Appvance.
About the Author –
Kevin Surace is a highly lauded entrepreneur and innovator. He’s been awarded 93 worldwide patents, and was Inc. Magazine Entrepreneur of the Year, CNBC Innovator of the Decade, a Davos World Economic Forum Tech Pioneer, and inducted into the RIT Innovation Hall of Fame. Kevin has held leadership roles with Serious Energy, Perfect Commerce, CommerceNet and General Magic and is credited with pioneering work on AI virtual assistants, smartphones, QuietRock and the Empire State Building windows energy retrofit.
