CV
Education, research interests, and publications.
Contact Information
| Name | Mingjie Shen |
| Professional Title | PhD Student, Electrical and Computer Engineering |
| shen497@purdue.edu | |
| Location | 465 Northwestern Ave, West Lafayette, Indiana 47907 |
Professional Summary
PhD student at Purdue University advised by Prof. Aravind Machiry. Research on software security, static program analysis, and AI for software engineering, with a focus on improving static analysis for large real-world software systems.
Education
Academic Interests
Software security: Vulnerability detection, embedded software security, secure software development
Static program analysis: Scalable and precise analysis of large real-world codebases, CodeQL, empirical studies of analysis tools
AI for software engineering: LLM-assisted false positive filtering of SAST results, LLM-assisted CodeQL query synthesis, LLM-assisted generation of symbolic transformation rules
Open-Source Impact
Upstream patches: 107 patches merged into open-source projects (including Apache NuttX, Contiki-NG, Mbed OS, RIOT-OS, SDL, and lwIP) and 16 further reported bugs fixed by maintainers, fixing 300+ defects across 55 projects found by my CodeQL study; 376 of 709 disclosed defects confirmed by maintainers. Full list: https://szsam.github.io/impact/
Publications
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2026 Democratizing False Positives Filtering Through Learning Assisted Reasoning
29th International Symposium on Research in Attacks, Intrusions and Defenses (RAID 2026), to appear
Mingjie Shen, Sai Ritvik Tanksalkar, Christophe Hauser, Aravind Machiry
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2025 Finding 709 Defects in 258 Projects: An Experience Report on Applying CodeQL to Open-Source Embedded Software (Experience Paper)
Proceedings of the ACM on Software Engineering, ISSTA 2025
Mingjie Shen, Akul Abhilash Pillai, Brian A. Yuan, James C. Davis, Aravind Machiry
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2024 Insights from Running 24 Static Analysis Tools on Open Source Software Repositories
Information Systems Security (ICISS 2024)
Fabiha Hashmat, Zeyad Alwaleed Aljaali, Mingjie Shen, Aravind Machiry
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2023 Towards Automated Identification of Layering Violations in Embedded Applications (WIP)
Languages, Compilers, and Tools for Embedded Systems (LCTES 2023)
Mingjie Shen, James C. Davis, Aravind Machiry