“Systemized” Program Analysis – A Big Data Perspective

Program analysis is widely used in various application areas to solve many practical problems, e.g., compiler optimization, bug/security vulnerability detection, program understanding, etc. In spite of significant achievements on static analysis, how to scale sophisticated static analysis to large-scale modern software (like Linux kernel) has been a key challenge for at least a couple of decades, due to the highly intensive computation and huge memory consumption. 

In this talk, I will discuss our Big Data system solutions for scalable and precise static analysis. Specifically, we revisit the scalability problem of sophisticated static analysis from a Big Data perspective. That is, we turn Big code analysis into Big Data analytics and leverage large-scale data processing techniques to solve static analysis problem. 

Bio:

Zhiqiang Zuo is now an assistant professor in Department of Computer Science and Technology, Nanjing University. He got his PhD degree from National University of Singapore, and did postdoc research in University of California, Irvine. His research interests span programming languages, software engineering, and Big Data systems. He is recently focusing on building Big Data supports for scalable program analysis, synthesis and SAT solving. He has published a series of work on top venues, such as OSDI, ASPLOS, EuroSys, PLDI, OOPSLA, etc.