I am a second-year Ph.D. student at the Department of Computer Science, the University of Hong Kong, advised by Prof. Chenxiong Qian. My research interests lie in the broad field of computer systems, with a major focus on enhancing the reliability, safety, and performance of operating systems.
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The University of Hong KongDepartment of Computer Science
Ph.D. StudentSep. 2023 - present -
Peking UniversityB.S. in Electronic Information EngineeringSep. 2019 - Jul. 2023 -
Peking UniversityB.S. in EconomicsSep. 2020 - Jul. 2023
Experience
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Microsoft Research AsiaResearch InternOct. 2022 - Jun. 2023
Honors & Awards
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Presidential PhD Scholarship, the University of Hong Kong2023
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Award for Scientific Research, Peking University2022
News
Selected Publications (view all )
![[<strong>TOSEM</strong>] WizardMerge - Save Us From Merging Without Any Clues](/assets/images/covers/wizardmerge.png)
[TOSEM] WizardMerge - Save Us From Merging Without Any Clues
Qingyu Zhang, Junzhe Li, Jiayi Lin, Jie Ding, Lateng Lin, Chenxiong Qian# (# corresponding author)
ACM Transactions on Software Engineering and Methodology 2025 SoftwareJournal
We present WizardMerge, an auxiliary tool that leverages merging results from Git to retrieve code block dependency on text and LLVM-IR level and provide suggestions for developers to resolve errors introduced by textual merging. Through the evaluation, we subjected WizardMerge to testing on 227 conflicts within five large-scale projects. The outcomes demonstrate that WizardMerge diminishes conflict merging time costs, achieving a 23.85% reduction. Beyond addressing conflicts, WizardMerge provides merging suggestions for over 70% of the code blocks potentially affected by the conflicts. Notably, WizardMerge exhibits the capability to identify conflict-unrelated code blocks that require manual intervention yet are harmfully applied by Git during the merging.
[TOSEM] WizardMerge - Save Us From Merging Without Any Clues
Qingyu Zhang, Junzhe Li, Jiayi Lin, Jie Ding, Lateng Lin, Chenxiong Qian# (# corresponding author)
ACM Transactions on Software Engineering and Methodology 2025 SoftwareJournal
We present WizardMerge, an auxiliary tool that leverages merging results from Git to retrieve code block dependency on text and LLVM-IR level and provide suggestions for developers to resolve errors introduced by textual merging. Through the evaluation, we subjected WizardMerge to testing on 227 conflicts within five large-scale projects. The outcomes demonstrate that WizardMerge diminishes conflict merging time costs, achieving a 23.85% reduction. Beyond addressing conflicts, WizardMerge provides merging suggestions for over 70% of the code blocks potentially affected by the conflicts. Notably, WizardMerge exhibits the capability to identify conflict-unrelated code blocks that require manual intervention yet are harmfully applied by Git during the merging.
![[<strong>EuroSys'25</strong>] Daredevil: Rescue Your Flash Storage from Inflexible Kernel Storage Stack](/assets/images/covers/daredevil.png)
[EuroSys'25] Daredevil: Rescue Your Flash Storage from Inflexible Kernel Storage Stack
Junzhe Li, Ran Shu, Jiayi Lin, Qingyu Zhang, Ziyue Yang, Jie Zhang, Yongqiang Xiong, Chenxiong Qian# (# corresponding author)
The Twentieth European Conference on Computer Systems (EuroSys'25) 2025 SystemConference
We propose daredevil, a novel kernel storage stack, which addresses the multi-tenancy issue by decoupling the static bindings and allowing full connectivity between cores and NQs. In this way, Daredevil grants multi-tenancy control the flexibility to freely route requests among NQs according to their SLAs. Moreover, it incorporates multi-tenancy-aware scheduling on NQs to facilitate efficient request routing. Our evaluation shows that Daredevil can reduce I/O request latency by up to 3-170× compared to current kernel storage stacks, while maintaining comparable throughput.
[EuroSys'25] Daredevil: Rescue Your Flash Storage from Inflexible Kernel Storage Stack
Junzhe Li, Ran Shu, Jiayi Lin, Qingyu Zhang, Ziyue Yang, Jie Zhang, Yongqiang Xiong, Chenxiong Qian# (# corresponding author)
The Twentieth European Conference on Computer Systems (EuroSys'25) 2025 SystemConference
We propose daredevil, a novel kernel storage stack, which addresses the multi-tenancy issue by decoupling the static bindings and allowing full connectivity between cores and NQs. In this way, Daredevil grants multi-tenancy control the flexibility to freely route requests among NQs according to their SLAs. Moreover, it incorporates multi-tenancy-aware scheduling on NQs to facilitate efficient request routing. Our evaluation shows that Daredevil can reduce I/O request latency by up to 3-170× compared to current kernel storage stacks, while maintaining comparable throughput.
![[<strong>NDSS'25</strong>] Automatic Library Fuzzing through API Relation Evolvement](/assets/images/covers/nexzzer.png)
[NDSS'25] Automatic Library Fuzzing through API Relation Evolvement
Jiayi Lin, Qingyu Zhang, Junzhe Li, Chenxin Sun, Hao Zhou, Changhua Luo#, Chenxiong Qian# (# corresponding author)
Network and Distributed System Security Symposium (NDSS) 2025 SecurityConference
This work proposes NEXZZER, a new fuzzer that automatically detects vulnerabilities in libraries. NEXZZER employs a hybrid relation learning strategy to continuously infer and evolve API relations, incorporating a novel driver architecture to augment the testing coverage of libraries and facilitate deep vulnerability discovery. We evaluated NEXZZER across 18 libraries and the Google Fuzzer Test Suite. The results demonstrate its considerable advantages in code coverage and vulnerability-finding capabilities compared to prior works. NEXZZER can also automatically identify and filter out most API misuse crashes. Moreover, NEXZZER discovered 27 previously unknown vulnerabilities in well-tested libraries, including OpenSSL and libpcre2. At the time of writing, developers have confirmed 24 sof them, and 9 were fixed because of our reports.
[NDSS'25] Automatic Library Fuzzing through API Relation Evolvement
Jiayi Lin, Qingyu Zhang, Junzhe Li, Chenxin Sun, Hao Zhou, Changhua Luo#, Chenxiong Qian# (# corresponding author)
Network and Distributed System Security Symposium (NDSS) 2025 SecurityConference
This work proposes NEXZZER, a new fuzzer that automatically detects vulnerabilities in libraries. NEXZZER employs a hybrid relation learning strategy to continuously infer and evolve API relations, incorporating a novel driver architecture to augment the testing coverage of libraries and facilitate deep vulnerability discovery. We evaluated NEXZZER across 18 libraries and the Google Fuzzer Test Suite. The results demonstrate its considerable advantages in code coverage and vulnerability-finding capabilities compared to prior works. NEXZZER can also automatically identify and filter out most API misuse crashes. Moreover, NEXZZER discovered 27 previously unknown vulnerabilities in well-tested libraries, including OpenSSL and libpcre2. At the time of writing, developers have confirmed 24 sof them, and 9 were fixed because of our reports.