Qing Liao

Qing Liao is a Researcher at Harbin Institute of Technology, specializing in software engineering with a focus on security, machine learning applications for code, and automation. His work bridges theoretical and practical challenges in modern software systems.

Research Interests:
Qing's research spans several interconnected areas:

• Software Security: Vulnerability detection, patch analysis, and configuration security.
• Machine Learning for Code: Application of ML models (e.g., transformers, graph networks) to code understanding, generation, and API recommendation.
• Program Analysis: Techniques for static analysis, browser fuzzing, and performance tuning.
• Automation: Tools for IaC generation, UI-to-code transformation, and configuration optimization.

Publication Trends (2022–2026):
His recent publications emphasize security (7/11 papers), particularly vulnerability detection using graph learning and static analysis. A secondary focus is ML-driven code automation (4/11 papers), including knowledge distillation, API generation, and UI-to-code systems. Work consistently targets real-world applicability, evidenced by industry-track publications at ASE/ICSE.