Qingkai Shi

Registered user since Fri 4 May 2018

Name:Qingkai Shi

Qingkai Shi is a Postdoc Research Associate in the department of computer science, Purdue University. His major research interest is the use of compiler techniques to ensure software reliability. He has published extensively at premium venues of programming languages (PLDI, OOPSLA), software engineering (ICSE, FSE, TSE, ISSTA), and cybersecurity (S&P). His research received many awards including ACM SIGSOFT Distinguished Paper Award and Hong Kong Ph.D. Fellowship. His research has led to the discovery of over a hundred software vulnerabilities in open-source software and has been successfully commercialized in Sourcebrella Inc, a static analysis tool vendor. Qingkai obtained his Ph.D. and B.S. from Nanjing University and the Hong Kong University of Science and Technology, respectively.

Affiliation:Ant Group
Research interests:Programming Language, Software Engineering, Cybersecurity


POPL 2022 Committee Member in Artifact Evaluation Committee within the Artifact Evaluation-track
SPLASH 2021 Author of Program Analysis via Efficient Symbolic Abstraction within the OOPSLA-track
PLDI 2021 Author of Path-Sensitive Sparse Analysis without Path Conditions within the PLDI-track
ESEC/FSE 2021 Author of Skeletal Approximation Enumeration for SMT Solver Testing within the Research Papers-track
ISSTA 2021 Author of Fuzzing SMT Solvers via Two-Dimensional Input Space Exploration within the Technical Papers-track
ISSTA 2020 Author of Fast Bit-Vector Satisfiability within the Technical Papers-track
Author of DeepGini: Prioritizing Massive Tests to Enhance the Robustness of Deep Neural Networks within the Technical Papers-track
Author of Test Recommendation System Based on Slicing Coverage Filtering within the Tool Demonstration-track
Author of Escaping Dependency Hell: Finding Build Dependency Errors with the Unified Dependency Graph within the Technical Papers-track
Author of Functional Code Clone Detection with Syntax and Semantics Fusion Learning within the Technical Papers-track
ASE 2019 Author of NeuralVis: Visualizing and Interpreting Deep Learning Models within the Demonstrations-track
ICSE 2020 Author of Pipelining Bottom-up Data Flow Analysis within the Technical Papers-track
Author of Conquering the Extensional Scalability Problem for Value-Flow Analysis Frameworks within the Technical Papers-track
ICSE 2019 Author of SMOKE: Scalable Path-Sensitive Memory Leak Detection for Millions of Lines of Code within the Technical Track-track
PLDI 2018 Author of Pinpoint: Fast and Precise Sparse Value Flow Analysis for Million Lines of Code within the PLDI Research Papers-track