Machine Learning on Source Code

Explore machine learning applications on source code in this 48-minute conference talk from GOTO Copenhagen 2018. Discover how source{d} is developing open-source tools to enable large-scale code analysis and machine learning on source code. Learn about their powerful system that can ingest all public git repositories, convert code into Abstract Syntax Trees (ASTs) ready for machine learning and other analyses, and expose it through a flexible API. Gain insights from Francesc Campoy, VP of Developer Relations at source{d}, as he delves into the potential of applying machine learning techniques to vast amounts of source code data. Understand the implications and possibilities of this technology for developers, researchers, and organizations working with large codebases.