Lingming Zhang
Associate Professor
Studies software engineering with AI, including code LLMs and agents for program repair and testing.
Lingming Zhang's lab at UIUC works on large language models, reasoning & agents and compilers & programming languages. 36 papers since 2023, 20 at top venues, mostly ICSE and NeurIPS. 10 PhD students and 5 master's students. 30 alumni, most often to Amazon and Google. Applications close .
At a glance
- papers, 2023–2026
- 9/ year
- at T1 venues
- 56%
- PhD students
- 10
- MS students
- 5
- alumni listed
- 30
Papers by yearfrom the lab homepage, 2023–2026
- 4 papers2026 · T1 2 · collecting
- 8 papers2025 · T1 5
- 10 papers2024 · T1 6
- 14 papers2023 · T1 7
T1 venuesOtherVenues: ICSE, NeurIPS, ICML, ACL, EMNLP
Research focuspaper titles that use each topic's words
- Large language models19 papers
- Reasoning & agents7 papers
- Compilers & programming languages4 papers
- testing2 papers
- studies software0 papers
Paper list
From the lab homepage.
20264 papers
- ICML (T1)CAPTCHA Solving for Native GUI Agents: Automated Reasoning-Action Data Generation and Self-Corrective Training
- Communications of the ACM, Research HighlightsCopiloting the Copilots for Automated Program Repair
- ACM Transactions on Software Engineering and MethodologyLarge Language Model-Based Agents for Software Engineering: A Survey
- ICML (T1)Toward Training Superintelligent Software Agents Through Self-Play SWE-RL
20258 papers
- Proceedings of ACM SIGSOFT International Symposium on the Foundations of Software EngineeringAgentless🐱: Demystifying LLM-based Software Engineering Agents
- Proceedings of International Conference on Architectural Support for Programming Languages and Operating SystemsKernelGPT: Enhanced Kernel Fuzzing via Large Language Models
- SOSP (T1)KNighter: Transforming Static Analysis with LLM-Synthesized Checkers
- Proceedings of the ACM International Symposium on Software Testing and AnalysisProductively Deploying Emerging Models on Emerging Platforms: A Top-Down Approach for Testing and Debugging
- NeurIPS (T1)PurpCode: Reasoning for Safer Code Generation
- NeurIPS (T1)SEC-bench: Automated Benchmarking of LLM Agents on Real-World Software Security Tasks
- NeurIPS (T1)SWE-RL: Advancing LLM Reasoning via Reinforcement Learning on Open Software Evolution
- EMNLP (T1)UniDebugger: Hierarchical Multi-Agent Framework for Unified Software Debugging
202410 papers
- ICML (T1)🎩 Magicoder: Empowering Code Generation with OSS-Instruct
- Proceedings of the ACM International Symposium on Software Testing and AnalysisAutomated Program Repair via Conversation: Fixing 162 out of 337 bugs for $0.42 each using ChatGPT
- NeurIPS (T1)Can LLMs Implicitly Learn Numeric Parameter Constraints in Data Science APIs?
- Proceedings of the Conference on Language ModelingEvaluating Language Models for Efficient Code Generation
- ICSE (T1)Large Language Models are Edge-Case Generators: Crafting Unusual Programs for Fuzzing Deep Learning Libraries
- NeurIPS (T1)SelfCodeAlign: Self-Alignment for Code Generation
- Proceedings of the Conference on Language ModelingTop Leaderboard Ranking = Top Coding Proficiency, Always? EvoEval: Evolving Coding Benchmarks via LLM
- ICSE (T1)Universal Fuzzing via Large Language Models
- Proceedings of the ACM SIGPLAN Conference on Object-Oriented Programming Systems, Languages, and ApplicationsWhite-box Compiler Fuzzing Empowered by Large Language Models
- ACL (T1)xFT: Unlocking the Power of Code Instruction Tuning by Simply Merging Upcycled Mixture-of-Experts
202314 papers
- ICSE (T1)(ICSE 2023), 13 pages, to appear
- Proceedings of ACM SIGSOFT International Symposium on the Foundations of Software EngineeringA Large-scale Empirical Review of Patch Correctness Checking Approaches
- ICSE (T1)Automated Program Repair in the Era of Large Pre-trained Language Models
- ICSE (T1)Balancing Effectiveness and Flakiness of Non-Deterministic Machine Learning Tests
- Proceedings of ACM SIGSOFT International Symposium on the Foundations of Software EngineeringCopiloting the Copilots: Fusing Large Language Models with Completion Engines for Automated Program Repair
- ICSE (T1)Fuzzing Automatic Differentiation in Deep-Learning Libraries
- NeurIPS (T1)Is Your Code Generated by ChatGPT Really Correct? Rigorous Evaluation of Large Language Models for Code Generation
- ICSE (T1)JITfuzz: Coverage-guided Fuzzing for JVM Just-in-Time Compilers
- Proceedings of the ACM International Symposium on Software Testing and AnalysisLarge Language Models are Zero-Shot Fuzzers: Fuzzing Deep-Learning Libraries via Large Language Models
- ICSE (T1)MirrorTaint: Practical Non-intrusive Dynamic Taint Tracking for JVM-based Microservice Systems
- Proceedings of ACM SIGSOFT International Symposium on the Foundations of Software EngineeringNeuRI: Diversifying DNN Generation via Inductive Rule Inference
- Proceedings of International Conference on Architectural Support for Programming Languages and Operating SystemsNNSmith: Generating Diverse and Valid Test Cases for Deep Learning Compilers
Where alumni went30 listed, 52% to industry
Industry 11Academia 10
Most often to
- Amazon
- Meta
Also to
- Tianjin University
- Carnegie Mellon University
- Kennesaw State University
Apply to UIUCFall 2027
UIUC Siebel Computer Science PhD
- Round
- Single round
- Opens
- —
- GRE
- optional
- English
- TOEFL iBT / IELTS
- Fee
- USD 90
Not yet confirmed for this intake.
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