WALA Everywhere

Thursday, August 9, 2018 -
11:00am to 12:00pm
2310 CS

Speaker Name: 

Julian Dolby

Speaker Institution: 

IBM Thomas J. Watson Research Center





Programming has embraced increasingly diverse languages across domains; indeed, every new domain seems to bring languages with it. There is enterprise software that is still heavily based in Java and C#, but Web programming is increasingly dominated by JavaScript, which is tightly integrated with HTML and is even making inroads on the server with nodejs. The increasing interest in machine learning has increased use of Python and its associated libraries. Furthermore, each language brings with it an ecosystem of tools, such Eclipse and Intellij for Java, Atom and Visual Studio Code for JavaScript, PyCharm and Jupyter for Python. And systems are increasingly used from a diverse array of platforms, including mobile devices.

This plethora poses a challenge for program analysis frameworks: many have traditionally been focused on a single system, such as bytecode analysis of Java, but, to be maximally useful, frameworks ideally would follow users when they employ different tools for different jobs. In this talk, I shall discuss how the Watson Libraries for Analysis (WALA), an open-source analysis framework, is designed for this world by enabling analysis of a variety of languages on a variety of platforms and for presenting analysis information in a wide variety of tools. I shall discuss the underlying analysis framework, and present demos of WALA in action in multiple contexts. I will focus most on our recent work in tools for machine learning, with analysis examples and demos of tools in operation.


Julian has been a Research Staff Member at IBM's Thomas J. Watson Research Center since 2000. He works on a range of topics, including static program analysis, software testing, the semantic web (AI) and programming technology support for machine learning. He has also worked on the Jikes Research Virtual Machine (Jikes RVM).

Julian was educated at the University of Wisconsin-Madison as an undergraduate, and at the University of Illinois at Urbana-Champaign as a graduate student where he worked with Professor Andrew Chien on programming systems for massively-parallel machines.