There was a lot of traffic to our booth at DVCon, San Jose February 26 - March 1, 2018, where we presented the new PinDown release that will perform automatic debug using machine learning, something that will dramatically speed up debug of regression failures. We also presented a new way of displaying test results that allows you to identify coverage holes and error-prone areas.

A lot of interest at our booth at DVCon

Machine Learning

The coming PinDown release will have an improved prediction model that is able to identify the risk level of each commit in the revision control system based on analysis of the revision control history as well as the code itself. We have used data that PinDown has stored locally in its database onsite at customers to train the prediction model to recognize high-risk commits. Debugging commits in order of risk allows PinDown to find the bugs much faster.

PinDown Using Machine Learning

Showing Test Results as a Cityscape

We also presented a poster titled An Analytical View of Test Results Using Cityscapes which shows a new cool way of analyzing test results. The intent of this view is to give the verification leads/managers a helicopter view of the status of the project. It shows test coverage holes and error-prone areas which then can be addressed. Here is a demo video:


Test Results as a Cityscape