[06-12]Model-based Analysis from Traditional to Intelligent Software
Title: Model-based Analysis from Traditional to Intelligent Software
Speaker: Xiaofei Xie
Time: 6月12日（周一） 15:00-16:00
Abstract: Over the past decade, the application of learning-based software in various domains, such as face recognition, autonomous driving, and content generation, has shown tremendous potential. The evolution of software has led to a diverse landscape, ranging from traditional code-based programs to AI-driven software (a.k.a., intelligent software). However, like traditional software, intelligent software can exhibit incorrect behaviors, which may result in severe accidents and losses. Ensuring the quality and security of software, particularly in safety- and security-critical scenarios, is of utmost importance. However, the black-box nature of intelligent software poses significant challenges in analyzing and explaining its behaviors. In this talk, I will present the model-based analysis from traditional software to deep learning-based software. Our approach involves constructing an abstract model for a given software (e.g., code or a deep neural network). Based on this model, we can perform comprehensive analysis, testing, fault localization, and automated repair to enhance its quality and security. This talk will focus more on the model-based analysis of intelligent software.
Bio: Dr. Xiaofei Xie is an assistant professor at Singapore Management University. He obtained his Ph.D from Tianjin University and won the CCF Outstanding Doctoral Dissertation Award (2019) in China. His research mainly focuses on the quality assurance of both traditional software and AI-enabled software. He has published some top-tier conference/journal papers in the areas of software engineering, security and AI, such as ICSE, ESEC/FSE, ISSTA, ASE, TSE, TOSEM, ICLR, NeurIPS, ICML, TPAMI, Usenix Security and CCS. In particular, he has received three ACM SIGSOFT Distinguished Paper Awards (FSE’16, ASE’19 and ISSTA’22).