[Online Talk] A Data-Driven Approach to Measuring E2E Test Coverage Using ML

This talk is FREE but please get tickets via http://bit.ly/prodperfecttalk. Only ticket holders will get the zoom link to the talk a couple of hours before it starts. We are also looking to record the talk and link to recording will also be sent to ticket holders.

Summary:

Two different teams of engineers, for two identical web applications, will (with great effort), build and maintain two very different E2E test suites. This means we don't have an agreed-upon method of deciding what is a testing priority, and what isn't. QA teams have been stuck dealing with this for 20 years; ProdPerfect and the introduction of ML as a whole are changing the game. By learning how your users use your application, machines can effectively decide what are and are not high-priority test cases, and further, deploy and maintain those tests. The result is both greater resource efficiency for your team and a test suite that can finally be both lean, and comprehensive, thanks to being built on data.

Speaker bio:

MIT Bacherlors/Masters grad, studying mechanical engineering and political science. 4 years’s experience as operations and engineering consultant with Stroud International, followed by operations/sales executive role at startup HelmetHub. Interludes as political author, business book ghost-writer, and consultant for private equity and biotech.

Erik’s big ambitions are to use artificial intelligence to help large organizations and societies consistently identify truth from falsehood, and make better fact-based decisions.