Machine Learning for Developer Productivity at Facebook
While machine learning has the potential to fundamentally improve how software is constructed, opportunities to leverage machine learning to improve conventional developer tools have gone untapped in practice. At Facebook, our developer infrastructure team is on a mission to rethink and retool Facebook’s developer toolchain by applying machine learning at every layer in our stack. Our goal is to make our developers more productive, and our processes and infrastructure more efficient, by integrating ML into our programming languages and developer tools, including IDEs, version control, or continuous integration systems, in novel ways. This talk will describe some of the work our team has been doing to improve developer efficiency and resource utilization at Facebook. I’ll also touch upon future opportunities we see to optimize or auto-tune other common pieces of developer infrastructure.
Satish Chandra obtained a PhD from the University of Wisconsin-Madison in 1997, and a B.Tech from the Indian Institute of Technology-Kanpur in 1991, both in computer science. From 1997 to 2002, he was a member of technical staff at Bell Laboratories, where his research focused on program analysis, domain-specific languages, and data-communication protocols. From 2002 to 2013, he was a research staff member at IBM Research, where his research focused on bug finding and verification, software synthesis, and test automation. His work on bug finding shipped in IBM’s Java static analysis product, and his work on test automation was adopted in IBM’s testing services offering. From 2013 to 2016, he worked at Samsung Research America, where he led the advanced programming tools research team. His work on memory profiling of web apps was included in Samsung’s Tizen IDE. In 2016, he started working at Facebook. He is an ACM Distinguished Scientist.
Mon 29 Jun Times are displayed in time zone: (UTC) Coordinated Universal Time change
|13:15 - 14:15|
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