Hello! This is Ling Jiang (蒋灵). I am a master student in Department of Computer Science and Engineering at Southern University of Science and Technology (SUSTech). I am a member of SUSTech ARiSE lab, advised by Prof. Yuqun Zhang. My research interest primarily lies in binary fuzzing and software composition analysis, and my research goal is to improve the reliability of software systems via program analysis and AI security.

I am admitted into Tencent Rhino Bird Elite Talent Training Program and interning at Tencent Keen Security Lab. More specifically, I am working on intelligent binary software composition analysis (BSCA) and reverse engineering based on function similarity model.

Feel free to drop me an email if we share common research interest

  • Software Testing
  • Software Composition Analysis
  • Reverse Engineering
  • AI for SE, Security
  • M.Eng. in Computer Science and Technology, 2024 (exp)

    Southern University of Science and Technology

  • B.Eng. in Computer Science and Technology, 2021

    Southern University of Science and Technology


(2023). BinaryAI: Binary Software Composition Analysis via Intelligent Binary Source Code Matching. ICSE 2024.

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(2023). Third-Party Library Dependency for Large-Scale SCA in the C/C++ Ecosystem: How Far Are We?. ISSTA 2023.

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(2023). Evaluating and Improving Hybrid Fuzzing. ICSE 2023.

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(2022). One Fuzzing Strategy to Rule Them All. ICSE 2022.

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(2022). Evaluating and Improving Neural Program-smoothing-based Fuzzing. ICSE 2022.

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Research Experience

  • 2022 Tencent Rhino-Bird Elite Training Program with distinguished scholarship
  • Designed new algorithms to construct third-party library dependency in C/C++ ecosystem using function-level clone detection and graph centrality analysis
  • Collaborated with Keen Security Lab to publish the state-of-the-art binary software composition analysis tool
  • Supervised by Tencent Security Researchers Sen Nie and Qiyi Tang
  • Worked on the effectiveness of binary fuzzing and proposed two fuzzing tools with increased performance, including evolutionary-based fuzzing and neural program-smoothing-based fuzzing
  • Conducted an extensive study of hybrid fuzzing and proposed a novel hybrid fuzzer (CoFuzz), which outperforms existing techniques and detects 8 new CVEs
  • Supervised by Prof. Yuqun Zhang

Industry Experience

Algorithm Engineer Intern
Aug 2023 – Present Beijing, China
  • Contributed to the development of the private code large language model (LLM)
  • Worked on intelligent tasks like code generation (including unit test and code review), code context-aware conversation, defect detection and repair, etc
Tencent Keen Security Lab
Security Engineer Intern
Jun 2022 – Aug 2023 Shanghai, China
  • Worked on BinaryAI, a binary file analysis platform developed by Keen Security Lab, which is based on static analysis and AI security
  • Contributed to the binary-to-source function similarity model based on LLMs, which powers BinaryAI
  • Designed and deployed new algorithms for the model’s downstream tasks, including software composition analysis (SCA), reverse engineering, binary diffing and malware analysis
Tencent PCG
Software Development Engineer Intern
Jul 2020 – Sep 2020 Shenzhen, China
  • Back-end development for Tencent QQ client, including social platform and value-added services
  • Built testing frameworks and CI/CD pipelines that automate software delivery