2025

[<strong>TOSEM</strong>] WizardMerge - Save Us From Merging Without Any Clues
[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.

[Code]

[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.

[Code]

[<strong>EuroSys'25</strong>] Daredevil: Rescue Your Flash Storage from Inflexible Kernel Storage Stack
[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.

[Code] [Paper] [Slides]

[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.

[Code] [Paper] [Slides]

[<strong>NDSS'25</strong>] Automatic Library Fuzzing through API Relation Evolvement
[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.

[Code] [Paper]

[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.

[Code] [Paper]

2022

[HotStorage'22] ScalaRAID: optimizing linux software RAID system for next-generation storage

Shushu Yi, Yanning Yang, Yunxiao Tang, Zixuan Zhou, Junzhe Li, Chen Yue, Myoungsoo Jung, Jie Zhang# (# corresponding author)

Proceedings of the 14th ACM Workshop on Hot Topics in Storage and File Systems (HotStorage'22) 2022 SystemConference

RAID has been widely adopted to enhance the performance, capacity, and reliability of the existing storage systems. However, we observe that the Linux software RAID (mdraid) suffers from its poor implementation of the lock mechanism. To address this, we propose ScalaRAID, which refines the role domain of locks and designs a new data structure to prevent different threads from preempting the RAID resources. By doing so, ScalaRAID can maximize the thread-level parallelism and reduce the time consumption of I/O request handling. Our evaluation results reveal that ScalaRAID can improve throughput by 89.4% while decreasing 99.99th percentile latency by 85.4% compared to mdraid.

[Code] [Paper]

[HotStorage'22] ScalaRAID: optimizing linux software RAID system for next-generation storage

Shushu Yi, Yanning Yang, Yunxiao Tang, Zixuan Zhou, Junzhe Li, Chen Yue, Myoungsoo Jung, Jie Zhang# (# corresponding author)

Proceedings of the 14th ACM Workshop on Hot Topics in Storage and File Systems (HotStorage'22) 2022 SystemConference

RAID has been widely adopted to enhance the performance, capacity, and reliability of the existing storage systems. However, we observe that the Linux software RAID (mdraid) suffers from its poor implementation of the lock mechanism. To address this, we propose ScalaRAID, which refines the role domain of locks and designs a new data structure to prevent different threads from preempting the RAID resources. By doing so, ScalaRAID can maximize the thread-level parallelism and reduce the time consumption of I/O request handling. Our evaluation results reveal that ScalaRAID can improve throughput by 89.4% while decreasing 99.99th percentile latency by 85.4% compared to mdraid.

[Code] [Paper]