Xin is a fourth-year PhD student at Georgia Tech working with Prof. Mayur Naik. He received his bachelor degree from Shanghai Jiaotong University in 2011. His research interests include program analysis, program verification, mobile-cloud computing and approximate computing. Currently his research mainly focuses on self-adaptive program analysis for large programs. He is a recipient of a PLDI Distinguished Paper Award (2014).

Research Summary

Automated program analysis has shown the ability to proven on-trivial properties of real-world programs. Due to the undecidable nature of these properties, program analysis involves making various trade-offs. Diverse artifacts built atop program analysis place divergent demands on these trade-offs. Therefore, to be effective, program analysis must be adapted to the client needs, in aspects of scalability, applicability, and accuracy. Today’s program analyses, however, do not provide useful tuning knobs on such aspects. As a result, effectively building artifacts atop current program analyses demands Herculean efforts or specialists knowing algorithmic and engineering aspects of program analysis. Xin’s research seeks to bridge the gap between program analyses and clients, by proposing a general computer-assisted approach to effectively adapt program analyses to diverse clients. His work casts adaptivity as a large-scale optimization problem, and solves it by applying novel search algorithms including iterative refinement, constraint solving and other techniques.