Hang analysis: fighting responsiveness bugs. - Xi Wang 005; Zhenyu Guo; Xuezheng Liu; Zhilei Xu; Haoxiang Lin; Xiaoge Wang; Zheng Zhang 001 (EuroSys, 2008)

MODIST: Transparent Model Checking of Unmodified Distributed Systems. - Junfeng Yang; Tisheng Chen; Ming Wu 007; Zhilei Xu; Xuezheng Liu; Haoxiang Lin; Mao Yang 004; Fan Long; Lintao Zhang; Lidong Zhou (NSDI, 2009)

Language-based replay via data flow cut. - Ming Wu 007; Fan Long; Xi Wang 005; Zhilei Xu; Haoxiang Lin; Xuezheng Liu; Zhenyu Guo; Huayang Guo; Lidong Zhou; Zheng Zhang 001 (SIGSOFT FSE, 2010)

G2: A Graph Processing System for Diagnosing Distributed Systems. - Zhenyu Guo; Dong Zhou 006; Haoxiang Lin; Mao Yang 004; Fan Long; Chaoqiang Deng; Changshu Liu; Lidong Zhou (USENIX ATC, 2011)

Optimizing Data Shuffling in Data-Parallel Computation by Understanding User-Defined Functions. - Jiaxing Zhang; Hucheng Zhou; Rishan Chen; Xuepeng Fan; Zhenyu Guo; Haoxiang Lin; Jack Li 001; Wei Lin 016; Jingren Zhou 001; Lidong Zhou (NSDI, 2012)

A characteristic study on failures of production distributed data-parallel programs. - Sihan Li; Hucheng Zhou; Haoxiang Lin; Tian Xiao; Haibo Lin; Wei Lin 016; Tao Xie 001 (ICSE, 2013)

A Constructive Proof On the Compositionality of Linearizability. - Haoxiang Lin (CoRR, 2014)

GraM: scaling graph computation to the trillions. - Ming Wu 007; Fan Yang 024; Jilong Xue; Wencong Xiao; Youshan Miao; Lan Wei; Haoxiang Lin; Yafei Dai; Lidong Zhou (SoCC, 2015)

An Empirical Study on Quality Issues of Production Big Data Platform. - Hucheng Zhou; Jian-Guang Lou; Hongyu Zhang 002; Haibo Lin; Haoxiang Lin; Tingting Qin (ICSE (2), 2015)

Well Extend Partial Well Orderings. - Haoxiang Lin (CoRR, 2015)

An empirical study on program failures of deep learning jobs. - Ru Zhang; Wencong Xiao; Hongyu Zhang 002; Yu Liu; Haoxiang Lin; Mao Yang 004 (ICSE, 2020)

Estimating GPU memory consumption of deep learning models. - Yanjie Gao; Yu Liu; Hongyu Zhang 002; Zhengxian Li; Yonghao Zhu; Haoxiang Lin; Mao Yang 004 (ESEC/SIGSOFT FSE, 2020)

Enhancing the interoperability between deep learning frameworks by model conversion. - Yu Liu; Cheng Chen; Ru Zhang; Tingting Qin; Xiang Ji; Haoxiang Lin; Mao Yang 004 (ESEC/SIGSOFT FSE, 2020)

Resource-Guided Configuration Space Reduction for Deep Learning Models. - Yanjie Gao; Yonghao Zhu; Hongyu Zhang 002; Haoxiang Lin; Mao Yang 004 (ICSE, 2021)

Tensor Network for Supervised Learning at Finite Temperature. - Haoxiang Lin; Shuqian Ye; Xi Zhu (CoRR, 2021)

REFTY: Refinement Types for Valid Deep Learning Models. - Yanjie Gao; Zhengxian Li; Haoxiang Lin; Hongyu Zhang 002; Ming Wu 007; Mao Yang 004 (ICSE, 2022)

GPU Occupancy Prediction of Deep Learning Models Using Graph Neural Network. - Hengquan Mei; Huaizhi Qu; Jingwei Sun 001; Yanjie Gao; Haoxiang Lin; Guangzhong Sun (CLUSTER, 2023)

Runtime Performance Prediction for Deep Learning Models with Graph Neural Network. - Yanjie Gao; Xianyu Gu; Hongyu Zhang 002; Haoxiang Lin; Mao Yang 004 (ICSE-SEIP, 2023)

An Empirical Study on Quality Issues of Deep Learning Platform. - Yanjie Gao; Xiaoxiang Shi; Haoxiang Lin; Hongyu Zhang 002; Hao Wu; Rui Li; Mao Yang 004 (ICSE-SEIP, 2023)

An Empirical Study on Low GPU Utilization of Deep Learning Jobs. - Yanjie Gao; Yichen He; Xinze Li; Bo Zhao; Haoxiang Lin; Yoyo Liang; Jing Zhong; Hongyu Zhang 002; Jingzhou Wang; Yonghua Zeng; Keli Gui; Jie Tong; et al. (ICSE, 2024)

dl²: Detecting Communication Deadlocks in Deep Learning Jobs. - Yanjie Gao; Jiyu Luo; Haoxiang Lin; Hongyu Zhang 002; Ming Wu 007; Mao Yang 004 (SIGSOFT FSE Companion, 2025)

An Empirical Study of Issues in Large Language Model Training Systems. - Yanjie Gao; Ruiming Lu; Haoxiang Lin; Yueguo Chen (SIGSOFT FSE Companion, 2025)

Reduction Fusion for Optimized Distributed Data-Parallel Computations via Inverse Recomputation. - Haoxiang Lin; Yang Wang 053; Yanjie Gao; Hongyu Zhang 002; Ming Wu 007; Mao Yang 004 (SIGSOFT FSE Companion, 2025)