With their AI, you can train mabl to go on a "user's journey". Much like Selenium IDE, the record-and-playback device, you walk the agent on what you as a tester would like it to focus on. What you want it to know. What you want it to ignore. It then learns and adapts, learning how you like to test.
Their product should be launching a free public beta in a few weeks. I can't wait to try it out!
Thank you so much, mabl for having us!
Intelligent Testing with mabl: Adaptive tests and automated visual insights
John Kinnebrew and Sergei Makar-Limanov
February 6th, 2018
About John Kinnebrew:
https://johnkinnebrew.com/
"John is building intelligent tools that enable developers to create better software at mabl. He received his B.A. in Computer Science from Harvard University and his Ph.D. in Computer Science from Vanderbilt University. His research interests include machine learning, combinatorial optimization, intelligent agents, and coordination in multi-agent systems.
"At Bridj, he led the research and data science group with a focus on machine learning and AI optimization techniques applied to transportation modeling, prediction, and optimization for improving mass transit. His research at Vanderbilt focused on the design of intelligent pedagogical agents and machine learning techniques to model important learning behaviors, including metacognition and self-regulated learning strategies, from activity traces of student interaction in educational systems".
Happy Testing!
-T.J. Maher
Sr. QA Engineer, Software Engineer in Test
Meetup Organizer, Ministry of Testing - Boston
Twitter | YouTube | LinkedIn | Articles
"John is building intelligent tools that enable developers to create better software at mabl. He received his B.A. in Computer Science from Harvard University and his Ph.D. in Computer Science from Vanderbilt University. His research interests include machine learning, combinatorial optimization, intelligent agents, and coordination in multi-agent systems.
"At Bridj, he led the research and data science group with a focus on machine learning and AI optimization techniques applied to transportation modeling, prediction, and optimization for improving mass transit. His research at Vanderbilt focused on the design of intelligent pedagogical agents and machine learning techniques to model important learning behaviors, including metacognition and self-regulated learning strategies, from activity traces of student interaction in educational systems".
Happy Testing!
-T.J. Maher
Sr. QA Engineer, Software Engineer in Test
Meetup Organizer, Ministry of Testing - Boston
Twitter | YouTube | LinkedIn | Articles
7 comments:
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