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The availability of large code corpora has made it possible to build innovative software productivity tools that use machine learning, paving way for capabilities that were not possible with static analysis technology. In this talk, I will provide a quick overview of this area, and include some industry experiences.
Satish Chandra is a researcher at Facebook, where he applies machine learning techniques to improve developer productivity. Prior to that, he has worked -- in reverse chronological order -- at Samsung Research, IBM Research, and Bell Laboratories. His work has spanned many areas of programming languages and software engineering, including program analysis, type systems, software synthesis, bug finding and repair, software testing and test automation, and web technologies. His research has been widely published in leading conferences in his field, including POPL, PLDI, ICSE, FSE and OOPSLA. The projects he has led have had significant industrial impact: in additional to his work on developer productivity at Facebook, his work on bug finding tools shipped in IBM's Java static analysis product, his work on test automation was adopted in IBM's testing services offering, and his work on memory profiling of web apps was included in Samsung's Tizen IDE. Satish Chandra obtained a PhD from the University of Wisconsin-Madison, and a B.Tech from the Indian Institute of Technology-Kanpur, both in computer science. He is an ACM Distinguished Scientist and an elected member of WG 2.4.