Datanami Managing Editor Alex Woodie recently interviewed Hans Dockter about how big data and AI supports the practice of Developer Productivity Engineering and the big role it will play in advancing the discipline in the future.
Woodie writes, “The company (Gradle) has established a data science team and rolled out the first AI-based product. Predictive Test Selection uses machine learning to predict which parts of the codebase are sensitive to change and which tests can be safely excluded from the DevOps lifecycle.
‘We can tell you, oh, you changed that part of the software. 9,000 of the 10,000 tests you don’t need to run, because we know from our data that those tests are completely insensitive to those areas of the code,’ Dockter says. ‘Only 1,000 tests are sensitive to this area, so let’s only run 1,000 out of 10,000 tests.’
Advanced analytics and AI are critical to making sense of observability data, Dockter says, and Gradle will have more AI and analytics products to help customers soon. “We think at scale, only with advanced analytics and machine learning, can you really get the full benefits from that data for your developers.”