Hello, I'm Luyao Ren （任路遥）.

- Education: B.S. (2020) and Ph.D. (2025) in Computer Science at Peking University. Advisor: Tao Xie 谢涛, Yingfei Xiong 熊英飞

- Research Interests: (Software Engineering / AI Infra) designing principles and building infrastructures for quality assurance of AI-oriented software systems such as AI compilers.

- R&D Experience: visiting scholar at CMU (advised by Shurui Zhou 周抒睿, Christian Kästner), HKUST (advised by Shing-Chi Cheung 張成志);
SWE intern at Google (AdMob Team), Microsoft (Xiaoice Team), Megvii/Face++ (4D Team, advised by Gang Yu 俞刚), ByteDance (AI Lab, advised by Lei Li 李磊).

- Algorithm Competition: ACM-ICPC (CHINA-Final 2016, Asia Regional 2015 - 2017), Gold Medal; NOI 2014, Silver Medal; APIO 2014, Gold Medal.


Email: rly AT pku.edu.cn

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My research work during Ph.D. targets enhancing the testing and debugging of AI models and AI systems, through techniques such as program analysis, constraint solving, and domain-specific languages. In addition to academic contributions, my work has successfully detected 50+ bugs in AI compilers and graph databases, and has been deployed in top-tier industrial companies.

Specification-Guided Testing and Debugging

- Validity-Preserving Delta Debugging via Generator Trace Reduction. TOSEM 2025.
Luyao Ren, Xing Zhang, Ziyue Hua, Yanyan Jiang, Xiao He, Yingfei Xiong, Tao Xie.

- Effective Random Test Generation for Deep Learning Compilers. SCIS 2024.
Luyao Ren, Ziheng Wang, Yingfei Xiong, Li Zhang, Guoyue Jiang, Tao Xie.

- GDsmith: Detecting Bugs in Cypher Graph Database Engines. ISSTA 2023.
Ziyue Hua, Wei Lin, Luyao Ren, Zongyang Li, Lu Zhang, Wenpin Jiao, Tao Xie.

- Input Reduction Enhanced LLM-based Program Repair.
Boyang Yang*, Luyao Ren*, Xin Yin, Jiadong Ren, Haoye Tian, Shunfu Jin.

- Invariants-awared Delta Debugging. (Draft)
Luyao Ren, Xing Zhang, Boyang Yang, Tao Xie.

Program Analysis on Neural Networks

- Detecting Numerical Bugs in Neural Network Architectures. ESEC/FSE 2020. Distinguished Paper Award.
Yuhao Zhang, Luyao Ren, Liqian Chen, Yingfei Xiong, Shing-Chi Cheung, Tao Xie.

- Reliability Assurance for Deep Neural Network Architectures Against Numerical Defects. ICSE 2023.
Linyi Li, Yuhao Zhang, Luyao Ren, Yingfei Xiong, Tao Xie.

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During my undergraduate, I research on fork-based development in open source software community, improving collaboration and identifying redundancies by machine learning techniques. In addition to academic contributions, I have built a prototype of our research work for detecting duplicate issues on GitHub before GitHub officially launching this feature. My experience has received a “like” from GitHub CEO on Twitter. Additionally, I have worked on program transformations and automated program repair, and developed an IntelliJ IDEA plugin named GenPater, which has been published on the JetBrains Marketplace.

Fork-based Development

- Identifying Redundancies in Fork-based Development. SANER 2019.
Luyao Ren, Shurui Zhou, Christian Kästner, Andrzej Wąsowski.

- Automated Patch Porting across Forked Projects. ESEC/FSE 2019 (SRC).
Luyao Ren.

- Forks Insight: Providing an Overview of GitHub Forks. ICSE 2018 (Poster). Tool
Luyao Ren, Shurui Zhou, Christian Kästner.

- Inferring Program Transformations From Singular Examples via Big Code. ASE 2019.
Jiajun Jiang, Luyao Ren, Yingfei Xiong, Lingming Zhang.