He, Pinjia

Drain: an online log parsing approach with fixed depth tree

He, P., Zhu, J., Zheng, Z., & Lyu, M. R. (2017) · 2017 IEEE International Conference on Web Services (ICWS)

Software System Performance and ReliabilityAdvanced Database Systems and Queries

Cited by 943

Tools and benchmarks for automated log parsing

Zhu, J., He, S., Liu, J., He, P., Xie, Q., Zheng, Z., & Lyu, M. R. (2019) · 2019 IEEE/ACM 41st International Conference on Software Engineering: Software Engineering in Practice (ICSE-SEIP)

Software System Performance and ReliabilitySoftware Engineering Research

Cited by 574

A Survey on Automated Log Analysis for Reliability Engineering

He, S., He, P., Chen, Z., Yang, T., Su, Y., & Lyu, M. R. (2022) · ACM Computing Surveys

Software System Performance and ReliabilitySoftware Reliability and Analysis Research

Cited by 334

Loghub: a large collection of system log datasets for AI-driven log analytics

Zhu, J., He, S., He, P., Liu, J., & Lyu, M. R. (2023) · 2023 IEEE 34th International Symposium on Software Reliability Engineering (ISSRE)

Software System Performance and ReliabilitySoftware Engineering Research

Cited by 302

Towards Automated Log Parsing for Large-Scale Log Data Analysis

He, P., Zhu, J., He, S., Li, J., & Lyu, M. R. (2018) · IEEE Transactions on Dependable and Secure Computing

Software System Performance and ReliabilityAdvanced Database Systems and Queries

Cited by 230

BiasAsker: Measuring the Bias in Conversational AI System

Wan, Y., Wang, W., He, P., Gu, J., Bai, H., & Lyu, M. R. (2023) · Proceedings of the 31st ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering

Ethics and Social Impacts of AIHate Speech and Cyberbullying Detection

Cited by 54

HeMiRCA: fine-grained root cause analysis for microservices with heterogeneous data sources

Zhu, Z., Lee, C., Tang, X., & He, P. (2024) · ACM Transactions on Software Engineering and Methodology

Software System Performance and ReliabilitySoftware Engineering Research

Cited by 26

MRCA: metric-level root cause analysis for microservices via multi-modal data

Wang, Y., Zhu, Z., Fu, Q., Ma, Y., & He, P. (2024) · Proceedings of the 39th IEEE/ACM International Conference on Automated Software Engineering

Software System Performance and ReliabilityCloud Computing and Resource Management

Cited by 18

GPT-4 Is Too Smart To Be Safe: Stealthy Chat with LLMs via Cipher

Yuan, Y., Jiao, W., Wang, W., Huang, J.-T., He, P., Shi, S., & Tu, Z. (2023) · arXiv

Adversarial Robustness in Machine LearningEthics and Social Impacts of AI

Cited by 14

SWE-ABS: Adversarial Benchmark Strengthening Exposes Inflated Success Rates on Test-based Benchmark

Yu, B., Cao, Y., Zhang, Y., Lin, L., Xu, J., Zhong, Z., Xu, Q., Wang, G., Cao, J., Cheung, S.-C., et al. (2026) · arXiv

Software Testing and Debugging TechniquesSoftware Engineering Research

Cited by 2

Human Cognitive Benchmarks Reveal Foundational Visual Gaps in MLLMs

Huang, J.-T., Dai, D., Huang, J.-Y., Yuan, Y., Liu, X., Wang, W., Jiao, W., He, P., Tu, Z., & Duan, H. (2025) · arXiv

Multimodal Machine Learning ApplicationsAdvanced Neural Network Applications

Cited by 2

SWE-Effi: Re-Evaluating Software AI Agent System Effectiveness Under Resource Constraints

Fan, Z., Vasilevski, K., Lin, D., Chen, B., Chen, Y., Zhong, Z., Zhang, J. M., He, P., & Hassan, A. E. (2025) · arXiv

Software Engineering ResearchSoftware Engineering Techniques and Practices

Cited by 0