2026 Lingming Zhang: Computer Science Researcher – H-Index, Publications & Awards

Discipline name	D-Index	World Ranking	Current World Ranking	National Ranking	Current National Ranking	Publications	Citations
Computer Science	50	5689	5529	2584	2494	122	8398

Overview

Lingming Zhang is affiliated with the University of Illinois at Urbana-Champaign in the United States. Their research primarily falls within the field of computer science, with a focus on software engineering and artificial intelligence. Zhang has a prolific publication record covering multiple subfields, including software, artificial intelligence, information systems, computer networks and communications, and signal processing.

The scientist's work has been featured extensively in several prominent venues. These include:

arXiv (Cornell University)
Zenodo (CERN European Organization for Nuclear Research)
Proceedings of the 44th International Conference on Software Engineering
Proceedings of the 30th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering
ACM Transactions on Software Engineering and Methodology

The main topics covered in their publications involve software testing and debugging techniques, software engineering research, software system performance and reliability, advanced malware detection techniques, software reliability and analysis research, adversarial robustness in machine learning, and machine learning and data classification. A complete list of these topics includes:

Software Testing and Debugging Techniques
Software Engineering Research
Software System Performance and Reliability
Advanced Malware Detection Techniques
Software Reliability and Analysis Research
Adversarial Robustness in Machine Learning
Machine Learning and Data Classification

Lingming Zhang's collaborative work includes frequent partnerships with several coauthors, namely Jiawei Liu, Yinlin Deng, Chunqiu Steven Xia, Yuxiang Wei, and Chenyuan Yang.

Recent publications by Zhang and collaborators include:

"Is Your Code Generated by ChatGPT Really Correct? Rigorous Evaluation of Large Language Models for Code Generation," 2023, published in arXiv (Cornell University)
"Less training, more repairing please: revisiting automated program repair via zero-shot learning," 2022, Proceedings of the 30th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering
"Practical Accuracy Estimation for Efficient Deep Neural Network Testing," 2020, ACM Transactions on Software Engineering and Methodology
"Free lunch for testing," 2022, Proceedings of the 44th International Conference on Software Engineering
"Fuzzing deep-learning libraries via automated relational API inference," 2022, Proceedings of the 30th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering

Best Publications

DeepRoad: GAN-based metamorphic testing and input validation framework for autonomous driving systems

Mengshi Zhang;Yuqun Zhang;Lingming Zhang;Cong Liu

619
Citations
Automated Program Repair in the Era of Large Pre-trained Language Models

Unknown

334
Citations
DeepFL: integrating multiple fault diagnosis dimensions for deep fault localization

Xia Li;Wei Li;Yuqun Zhang;Lingming Zhang

282
Citations
Is Your Code Generated by ChatGPT Really Correct? Rigorous Evaluation of Large Language Models for Code Generation

Unknown

263
Citations
Less training, more repairing please: revisiting automated program repair via zero-shot learning

Unknown

244
Citations
Automated Program Repair via Conversation: Fixing 162 out of 337 Bugs for $0.42 Each using ChatGPT

Unknown

231
Citations
Fuzz4ALL: Universal Fuzzing with Large Language Models

Unknown

189
Citations
An extensive study on pre-trained models for program understanding and generation

Unknown

187
Citations
Bridging the gap between the total and additional test-case prioritization strategies

Lingming Zhang;Dan Hao;Lu Zhang;Gregg Rothermel

183
Citations
Predictive Mutation Testing

Jie Zhang;Lingming Zhang;Mark Harman;Dan Hao

183
Citations
A Static Approach to Prioritizing JUnit Test Cases

Hong Mei;Dan Hao;Lingming Zhang;Lu Zhang

180
Citations
Practical program repair via bytecode mutation

Ali Ghanbari;Samuel Benton;Lingming Zhang

176
Citations
An extensive study of static regression test selection in modern software evolution

Owolabi Legunsen;Farah Hariri;August Shi;Yafeng Lu

152
Citations
Test generation via Dynamic Symbolic Execution for mutation testing

Lingming Zhang;Tao Xie;Lu Zhang;Nikolai Tillmann

144
Citations
DeepBillboard: systematic physical-world testing of autonomous driving systems

Husheng Zhou;Wei Li;Zelun Kong;Junfeng Guo

143
Citations
Boosting spectrum-based fault localization using PageRank

Mengshi Zhang;Xia Li;Lingming Zhang;Sarfraz Khurshid

140
Citations
Transforming Programs and Tests in Tandem for Fault Localization

Xia Li;Lingming Zhang

139
Citations
A Unified Test Case Prioritization Approach

Dan Hao;Lingming Zhang;Lu Zhang;Gregg Rothermel

135
Citations
Localizing failure-inducing program edits based on spectrum information

Lingming Zhang;Miryung Kim;Sarfraz Khurshid

132
Citations
Free Lunch for Testing: Fuzzing Deep-Learning Libraries from Open Source

Unknown

132
Citations
Boosting coverage-based fault localization via graph-based representation learning

Yiling Lou;Qihao Zhu;Jinhao Dong;Xia Li

126
Citations
An information retrieval approach for regression test prioritization based on program changes

Ripon K. Saha;Lingming Zhang;Sarfraz Khurshid;Dewayne E. Perry

125
Citations
How does regression test prioritization perform in real-world software evolution?

Yafeng Lu;Yiling Lou;Shiyang Cheng;Lingming Zhang

123
Citations
Operator-based and random mutant selection: better together

Lingming Zhang;Milos Gligoric;Darko Marinov;Sarfraz Khurshid

109
Citations
Hybrid regression test selection

Lingming Zhang

108
Citations
Faster mutation testing inspired by test prioritization and reduction

Lingming Zhang;Darko Marinov;Sarfraz Khurshid

108
Citations
Predictive mutation testing

Jie Zhang;Ziyi Wang;Lingming Zhang;Dan Hao

91
Citations

Frequent Co-Authors

Lu Zhang Peking University

Dan Hao Peking University

Sarfraz Khurshid The University of Texas at Austin

Darko Marinov University of Illinois at Urbana-Champaign

Hong Mei Peking University

Miryung Kim University of California, Los Angeles

Yingfei Xiong Peking University

Gregg Rothermel North Carolina State University

W. Eric Wong The University of Texas at Dallas

Bei Yu Chinese University of Hong Kong

External Links

Personal website of Lingming Zhang

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