I am a Ph.D student in the School of Electrical Engineering and Computer Science (EECS) at Washington State University, Pullman. My advisor is Prof. Haipeng Cai. My research interests are in Software Engineering and Software Security.

Research Projects:

Automatically generating realistic vulnerabilities for vulnerability analysis
Large-scale, realistic vulnerability datasets are essential for both benchmarking existing techniques and developing effective data-driven approaches for software security. In this project, we explore how to automatically generate realistic vulnerabilities by injecting vulnerabilities to the widely available real-world normal programs, through deep learning-based and pattern-based code editors.

On the Open Science of Deep Learning-Based Vulnerability Detection
Open science is a practice that makes scientific research publicly accessible to anyone, hence is highly beneficial. Given that an increasing number of deep learning-based vulnerability detection approaches are explored, we exhaustively search the literature in this area and comprehensively investigate the four integral aspects of open science: availability, executability, reproducibility, and replicability.

Evaluating and comparing memory error vulnerability detectors
An empirical study that evaluates and compares state-of-the-art memory error vulnerability detectors against publicly available benchmark datasets of C/C++ programs, with case studies to gain in-depth explanations of successes and failures of individual tools.