Machine learning for continuous integration
Your continuous integration process produces torrents of data. Joseph Gregorio explains how to mine that data to drive improvements in your development process and offers an overview of Skiaan open source 2D graphics library that provides common APIs that work across a variety of hardware and software platforms.
| Talk Title | Machine learning for continuous integration |
| Speakers | Joseph Gregorio (Google) |
| Conference | O’Reilly Open Source Convention |
| Conf Tag | Put open source to work |
| Location | Portland, Oregon |
| Date | July 16-19, 2018 |
| URL | Talk Page |
| Slides | Talk Slides |
| Video | |
Your continuous integration process produces torrents of data. Joseph Gregorio explains how to mine that data to drive improvements in your development process. Joseph offers an overview of Skia—an open source 2D graphics library that provides common APIs that work across a variety of hardware and software platforms—and demonstrates how it uses softmax regression and k-means clustering to improve its software development cycle. Skia serves as the graphics engine for Google Chrome and Chrome OS, Android, Mozilla Firefox and Firefox OS, and many other products. Testing Skia requires running a large number of correctness and performance tests over a large number of devices and operating systems, including Android, iOS, Windows, Linux, and macOS. The combinatorics involved mean that a huge amount of test data is produced (e.g., 30 million rendered images and 7.5 million performance metrics). Joseph outlines the ways Skia has mined that data using Go and TensorFlow to guard against regressions and discusses how other testing data is used to predict which tests should be run first to speed up development feedback.
