Results


Appvance Launches AI-Driven Test Automation

#artificialintelligence

By modeling human testers, including manual and test automation tasks such as scripting, Appvance has developed algorithms and expert systems to take on those tasks, similar to how driverless vehicle software models what a human driver does. The Appvance AI technology learns from various existing data sources, including learning to map an application fully on its own, various server logs, Splunk or Sumo Logic production data, form input data, valid headers and requests, expected responses, changes in each build and others. The resulting test execution represented real user flows, data driven, with near 100% code coverage. Built from the ground up with DevOps, agile and cloud services in mind, Appvance offers true beginning-to-end data-driven functional, performance, compatibility, security and synthetic APM test automation and execution, enabling dev and QA teams to quickly identify issues in a fraction of the time of other test automation products.


Evaluating Data Science Projects: A Case Study Critique

@machinelearnbot

By convention, the rare class is usually positive, so this means the True Positive (TP) rate is 0.78, and the False Negative rate (1 – True Positive rate) is 0.22. The Non-Large Loss recognition rate is 0.79, so the True Negative rate is 0.79 and the False Positive (FP) rate is 0.21. They don't report a False Positive rate (or True Negative rate, from which we could have calculated it). This result means that, using their Neural network, they must process 28 uninteresting Non-Large Loss customers (false alarms) for each Large-Loss customer they want.