LLM-Empowered Agentic Systems for Software Engineering Survey

Illustrations of process of LLM-empowered software engineering

About This Project

This is a continuously updated repository of papers and resources focused on LLM-Empowered Agentic Systems for Software Engineering.

For detailed content, please refer to our survey paper: "A Comprehensive Survey on Benchmarks and Solutions in Software Engineering of LLM-Empowered Agentic System"

If you find this project helpful for your research, please consider citing our work:

@misc{guo2025comprehensivesurveybenchmarkssolutions,
      title={A Comprehensive Survey on Benchmarks and Solutions in Software Engineering of LLM-Empowered Agentic System}, 
      author={Jiale Guo and Suizhi Huang and Mei Li and Dong Huang and Xingsheng Chen and Regina Zhang and Zhijiang Guo and Han Yu and Siu-Ming Yiu and Pietro Lio and Kwok-Yan Lam},
      year={2025},
      eprint={2510.09721},
      archivePrefix={arXiv},
      primaryClass={cs.SE},
      url={https://arxiv.org/abs/2510.09721}, 
}

Table of Contents

Pipeline of our work.

• Solutions
  • Prompt-Based
  • Fine-Tune-Based
  • Agent-Based
• Benchmarks
  • Code Generation
  • Code Translation
  • Program Repair
  • Others
• Applications
  • Requirements Analysis & Software Design
  • Code Generation & Transformation
  • Bug Repair & Debugging
  • Code Refactoring & Quality Improvement
  • Testing, Verification, & Security

Paper

Solutions

The components of different types of solutions.

Prompt-Based

• The first prompt counts the most! an evaluation of large language models on iterative example-based code generation, PACMSE, 2025
• An exploratory study on architectural smell refactoring using Large Languages Models, IEEE ICSA-C, 2025
• Demystifying Feature Requests: Leveraging LLMs to Refine Feature Requests in Open-Source Software, Requirements Engineering, 2025
• AdaptiveLLM: A Framework for Selecting Optimal Cost-Efficient LLM for Code-Generation Based on CoT Length, Internetware, 2025
• Revisiting Chain-of-Thought in Code Generation: Do Language Models Need to Learn Reasoning before Coding?, ICML, 2025
• SynFix: Dependency-aware program repair via RelationGraph analysis, ACL, 2025
• LLM4TDG: test-driven generation of large language models based on enhanced constraint reasoning, Cybersecurity, 2025
• Can Long-Context Language Models Solve Repository-Level Code Generation?, LTI Student Research Symposium 2025, 2025
• SynFix: Dependency-aware program repair via RelationGraph analysis, ACL, 2025
• LLM4TDG: test-driven generation of large language models based on enhanced constraint reasoning, Cybersecurity, 2025
• Codes: Natural language to code repository via multi-layer sketch, TOSEM, 2024
• Test-driven development and llm-based code generation, ASE, 2024
• PACGBI: A Pipeline for Automated Code Generation from Backlog Items, ASE, 2024
• Do Current Language Models Support Code Intelligence for R Programming Language?, TOSEM, 2024
• Improving Repository-level Code Search with Text Conversion, NAACL, 2024
• Codes: Natural language to code repository via multi-layer sketch, TOSEM, 2024

Fine-Tune-Based

• SEAlign: Alignment training for software engineering agent, ICSE, 2026
• SWE-GPT: A Process-Centric Language Model for Automated Software Improvement, PACMSE, 2025
• D3: A Dataset for Training Code LMs to Act Diff-by-Diff, COLM, 2025
• Training Software Engineering Agents and Verifiers with SWE-Gym, ICML, 2025
• SWE-Dev: Building Software Engineering Agents with Training and Inference Scaling, ACL, 2025
• Building A Proof-Oriented Programmer That Is 64% Better Than GPT-4o Under Data Scarcity, ACL, 2025
• MORepair: Teaching LLMs to Repair Code via Multi-Objective Fine-Tuning, TOSEM, 2025
• BLAZE: Cross-language and cross-project bug localization via dynamic chunking and hard example learning, TSE, 2025
• Improving fim code completions via context & curriculum based learning, WSDM, 2025
• Automated High-Level Code Optimization for Warehouse Performance, IEEE Micro, 2025
• Smaller but Better: Self-Paced Knowledge Distillation for Lightweight yet Effective LCMs, PACMSE, 2025
• Risk Assessment Framework for Code LLMs via Leveraging Internal States, FSE, 2025
• Code Graph Model (CGM): A Graph-Integrated Large Language Model for Repository-Level Software Engineering Tasks, NeurIPS, 2025
• Do Code Semantics Help? A Comprehensive Study on Execution Trace-Based Information for Code Large Language Models, EMNLP, 2025
• Co-PatcheR: Collaborative Software Patching with Component (s)-specific Small Reasoning Models, NeurIPS, 2025
• On Pretraining For Project-Level Code Completion, ICLR 2025 DL4C, 2025
• Reasoning Through Execution: Unifying Process and Outcome Rewards for Code Generation, ICML, 2025
• Kimi k2: Open agentic intelligence, arXiv preprint arXiv:2507.20534, 2025
• Optimizing case-based reasoning system for functional test script generation with large language models, ACM SIGKDD, 2025
• Teaching an Old LLM Secure Coding: Localized Preference Optimization on Distilled Preferences, ACL, 2025
• Can It Edit? Evaluating the Ability of Large Language Models to Follow Code Editing Instructions, COLM, 2024
• Rest-mcts*: Llm self-training via process reward guided tree search, NeurIPS, 2024
• Selfcodealign: Self-alignment for code generation, NeurIPS, 2024

Agent-Based

• Agilecoder: Dynamic collaborative agents for software development based on agile methodology, FORGE, 2025
• Artemis ai: Multi-llm framework for code optimisation, CAI, 2025
• Long Context Scaling: Divide and Conquer via Multi-Agent Question-driven Collaboration, ICML, 2025
• Co-PatcheR: Collaborative Software Patching with Component (s)-specific Small Reasoning Models, NeurIPS, 2025
• You name it, I run it: An LLM agent to execute tests of arbitrary projects, PACMSE, 2025
• ExploraCoder: Advancing Code Generation for Multiple Unseen APIs via Planning and Chained Exploration, ACL, 2025
• Improving Automated Issue Resolution via Comprehensive Repository Exploration, ICLR 2025 DL4C, 2025
• AlphaVerus: Bootstrapping Formally Verified Code Generation through Self-Improving Translation and Treefinement, ICML, 2025
• Demystifying llm-based software engineering agents, PACMSE, 2025
• PatchPilot: A Cost-Efficient Software Engineering Agent with Early Attempts on Formal Verification, ICML, 2025
• Interactive debugging and steering of multi-agent ai systems, CHI, 2025
• PATCHAGENT: a practical program repair agent mimicking human expertise, USENIX Conference on Security Symposium, 2025
• SWE-Dev: Building Software Engineering Agents with Training and Inference Scaling, ACL, 2025
• An LLM-based Agent for Reliable Docker Environment Configuration, NeurIPS, 2025
• Are" Solved Issues" in SWE-bench Really Solved Correctly? An Empirical Study, arXiv preprint arXiv:2503.15223, 2025
• Human-in-the-loop software development agents, ICSE-SEIP, 2025
• ROSE: An IDE-Based Interactive Repair Framework for Debugging, TOSEM, 2025
• Nemotron-CORTEXA: Enhancing LLM Agents for Software Engineering Tasks via Improved Localization and Solution Diversity, ICML, 2025
• On Simulation-Guided LLM-based Code Generation for Safe Autonomous Driving Software, EASE, 2025
• Assistance or disruption? exploring and evaluating the design and trade-offs of proactive ai programming support, CHI, 2025
• LLM4TDG: test-driven generation of large language models based on enhanced constraint reasoning, Cybersecurity, 2025
• CodeV: Issue Resolving with Visual Data, ACL, 2025
• Alibaba lingmaagent: Improving automated issue resolution via comprehensive repository exploration, FSE, 2025
• Code Graph Model (CGM): A Graph-Integrated Large Language Model for Repository-Level Software Engineering Tasks, NeurIPS, 2025
• Towards detecting prompt knowledge gaps for improved llm-guided issue resolution, MSR, 2025
• BLAZE: Cross-language and cross-project bug localization via dynamic chunking and hard example learning, TSE, 2025
• AEGIS: An Agent-based Framework for Bug Reproduction from Issue Descriptions, FSE, 2025
• Enhancing Human-IDE Interaction in the SDLC using LLM-based Mediator Agents, FSE, 2025
• Automated Generation of Issue-Reproducing Tests by Combining LLMs and Search-Based Testing, ASE, 2025
• Coding Agents with Multimodal Browsing are Generalist Problem Solvers, ICML 2025 Workshop on Computer Use Agents, Unknown Year
• Training Software Engineering Agents and Verifiers with SWE-Gym, ICML, 2025
• Kimi k2: Open agentic intelligence, arXiv preprint arXiv:2507.20534, 2025
• Magis: Llm-based multi-agent framework for github issue resolution, NeurIPS, 2024
• MASAI: Modular Architecture for Software-engineering AI Agents, NeurIPS 2024 Workshop on Open-World Agents, 2024
• Codes: Natural language to code repository via multi-layer sketch, TOSEM, 2024
• LLMDFA: analyzing dataflow in code with large language models, NeurIPS, 2024
• Enhancing automated program repair with solution design, ASE, 2024
• Marscode agent: Ai-native automated bug fixing, arXiv preprint arXiv:2409.00899, 2024
• Building ai agents for autonomous clouds: Challenges and design principles, SoCC, 2024
• How to understand whole software repository, arXiv preprint arXiv:2406.01422, 2024
• Autocoderover: Autonomous program improvement, ISSTA, 2024
• Evaluating human-ai partnership for llm-based code migration, CHI EA, 2024
• Self-taught optimizer (stop): Recursively self-improving code generation, First Conference on Language Modeling, 2024
• What's wrong with your code generated by large language models? an extensive study, arXiv preprint arXiv:2407.06153, 2024
• On coordinating llms and platform knowledge for software modernization and new developments, SSE, 2024
• Evor: Evolving retrieval for code generation, EMNLP, 2024
• Swe-agent: Agent-computer interfaces enable automated software engineering, NeurIPS, 2024

Benchmarks

Code Generation

• McEval: Massively Multilingual Code Evaluation, ICLR, 2025
• HumanEval Pro and MBPP Pro: Evaluating Large Language Models on Self-invoking Code Generation Task, ACL 2025 Findings, 2025
• BigCodeBench: Benchmarking Code Generation with Diverse Function Calls and Complex Instructions, ILCR, 2025
• EffiBench-X: A Multi-Language Benchmark for Measuring Efficiency of LLM-Generated Code, NeurIPS, 2025
• CLEVER: A Curated Benchmark for Formally Verified Code Generation, NeurIPS, 2025
• Automated Benchmark Generation for Repository-Level Coding Tasks, Forty-second International Conference on Machine Learning, 2025
• Can Language Models Replace Programmers for Coding? REPOCOD Says `Not Yet', Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2025
• HumanEvo: An Evolution-Aware Benchmark for More Realistic Evaluation of Repository-Level Code Generation, ICSE, 2025
• SWE-smith: Scaling Data for Software Engineering Agents, NeurIPS, 2025
• SWE-rebench: An Automated Pipeline for Task Collection and Decontaminated Evaluation of Software Engineering Agents, NeurIPS, 2025
• DyCodeEval: Dynamic Benchmarking of Reasoning Capabilities in Code Large Language Models Under Data Contamination, Forty-second International Conference on Machine Learning, 2025
• HumanEvalComm: Benchmarking the Communication Competence of Code Generation for LLMs and LLM Agents, ACM Trans. Softw. Eng. Methodol., 2025
• The first prompt counts the most! an evaluation of large language models on iterative example-based code generation, PACMSE, 2025
• Maintaincoder: Maintainable code generation under dynamic requirements, NeurIPS, 2025
• Seccodeplt: A unified platform for evaluating the security of code genai, NeurIPS, 2025
• CodeScope: An Execution-based Multilingual Multitask Multidimensional Benchmark for Evaluating LLMs on Code Understanding and Generation, ACL, 2024
• Evaluating Large Language Models in Class-Level Code Generation, Proceedings of the IEEE/ACM 46th International Conference on Software Engineering, 2024
• Evaluation of LLMs on Syntax-Aware Code Fill-in-the-Middle Tasks, Forty-first International Conference on Machine Learning, 2024
• DevEval: A Manually-Annotated Code Generation Benchmark Aligned with Real-World Code Repositories, ACL 2024 Findings, 2024
• R2e: Turning any github repository into a programming agent environment, ICML, 2024
• XCodeEval: An Execution-based Large Scale Multilingual Multitask Benchmark for Code Understanding, Generation, Translation and Retrieval, ACL, 2024
• CrossCodeBench: Benchmarking Cross-Task Generalization of Source Code Models, ICSE, 2023
• Is your code generated by ChatGPT really correct? rigorous evaluation of large language models for code generation, NeurIPS, 2023
• CodeGeeX: A Pre-Trained Model for Code Generation with Multilingual Benchmarking on HumanEval-X, Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2023
• Evaluating large language models trained on code, arXiv preprint arXiv:2107.03374, 2021
• Program synthesis with large language models, arXiv preprint arXiv:2108.07732, 2021
• CodeXGLUE: A Machine Learning Benchmark Dataset for Code Understanding and Generation, NeurIPS, 2021
• CodeNet: A Large-Scale AI for Code Dataset for Learning a Diversity of Coding Tasks, NeurIPS, 2021

Code Translation

• CRUST-Bench: A Comprehensive Benchmark for C-to-safe-Rust Transpilation, Second Conference on Language Modeling, 2025
• AlphaTrans: A Neuro-Symbolic Compositional Approach for Repository-Level Code Translation and Validation, Proc. ACM Softw. Eng., 2025
• Function-to-Style Guidance of LLMs for Code Translation, ICML, 2025
• CodeEditorBench: Evaluating Code Editing Capability of LLMs, ICLR 2025 DL4C, 2025
• Unraveling the Potential of Large Language Models in Code Translation: How Far are We?, 2024 31st Asia-Pacific Software Engineering Conference (APSEC), 2024
• XCodeEval: An Execution-based Large Scale Multilingual Multitask Benchmark for Code Understanding, Generation, Translation and Retrieval, ACL, 2024
• Evaluating Large Language Models in Class-Level Code Generation, Proceedings of the IEEE/ACM 46th International Conference on Software Engineering, 2024
• Repository-level code translation benchmark targeting rust, arXiv preprint arXiv:2411.13990, 2024
• Repotransbench: A real-world benchmark for repository-level code translation, arXiv preprint arXiv:2412.17744, 2024
• AVATAR: A Parallel Corpus for Java-Python Program Translation, Findings of the Association for Computational Linguistics: ACL 2023, 2023
• CodeTransOcean: A Comprehensive Multilingual Benchmark for Code Translation, EMNLP 2023 Findings, 2023
• CodeGeeX: A Pre-Trained Model for Code Generation with Multilingual Benchmarking on HumanEval-X, KDD, 2023
• On the Evaluation of Neural Code Translation: Taxonomy and Benchmark, ASE, 2023
• CrossCodeBench: Benchmarking Cross-Task Generalization of Source Code Models, Proceedings of the 45th International Conference on Software Engineering, 2023
• CodeXGLUE: A Machine Learning Benchmark Dataset for Code Understanding and Generation, NeurIPS, 2021
• CodeNet: A Large-Scale AI for Code Dataset for Learning a Diversity of Coding Tasks, NeurIPS, 2021

Program Repair

• CodeEditorBench: Evaluating Code Editing Capability of LLMs, ICLR 2025 DL4C, 2025
• RepoDebug: Repository-Level Multi-Task and Multi-Language Debugging Evaluation of Large Language Models, EMNLP, 2025
• LongCodeBench: Evaluating Coding LLMs at 1M Context Windows, Second Conference on Language Modeling, 2025
• Breakpoint: Stress-testing systems-level reasoning in LLM agents, Second Conference on Language Modeling, 2025
• Multi-SWE-bench: A Multilingual Benchmark for Issue Resolving, NeurIPS, 2025
• OmniGIRL: A Multilingual and Multimodal Benchmark for GitHub Issue Resolution, Proc. ACM Softw. Eng., 2025
• CVE-Bench: Benchmarking LLM-based Software Engineering Agent's Ability to Repair Real-World CVE Vulnerabilities, NAACL, 2025
• SEC-bench: Automated Benchmarking of LLM Agents on Real-World Software Security Tasks, NeurIPS, 2025
• Seccodeplt: A unified platform for evaluating the security of code genai, NeurIPS, 2025
• DebugBench: Evaluating Debugging Capability of Large Language Models, Findings of the Association for Computational Linguistics: ACL 2024, 2024
• SWE-bench: Can Language Models Resolve Real-world Github Issues?, ILCR, 2024
• When Large Language Models Confront Repository-Level Automatic Program Repair: How Well They Done?, ICSE-Companion, 2024
• The GitHub Recent Bugs Dataset for Evaluating LLM-Based Debugging Applications, ICST, 2024
• CodeXGLUE: A Machine Learning Benchmark Dataset for Code Understanding and Generation, NeurIPS, 2021
• CodeNet: A Large-Scale AI for Code Dataset for Learning a Diversity of Coding Tasks, NeurIPS, 2021

Others

• CRUXEVAL-X: A Benchmark for Multilingual Code Reasoning, Understanding and Execution, ACL, 2025
• CodeJudge-Eval: Can Large Language Models be Good Judges in Code Understanding?, COLING, 2025
• Evaluating Program Semantics Reasoning with Type Inference in System F, NeurIPS, 2025
• CORE: Benchmarking LLMs' Code Reasoning Capabilities through Static Analysis Tasks, NeurIPS, 2025
• TestEval: Benchmarking Large Language Models for Test Case Generation, NAACL 2025 Findings, 2025
• TestGenEval: A Real World Unit Test Generation and Test Completion Benchmark, ILCR, 2025
• RefactorBench: Evaluating Stateful Reasoning in Language Agents Through Code, ILCR, 2025
• GSO: Challenging Software Optimization Tasks for Evaluating SWE-Agents, NeurIPS, 2025
• DI-BENCH: Benchmarking Large Language Models on Dependency Inference with Testable Repositories at Scale, ACL 2025 Findings, 2025
• Beyond pip Install: Evaluating LLM Agents for the Automated Installation of Python Projects, SANER, 2025
• SyncMind: Measuring Agent Out-of-Sync Recovery in Collaborative Software Engineering, Forty-second International Conference on Machine Learning, 2025
• CRUXEval: A Benchmark for Code Reasoning, Understanding and Execution, ICML, 2024
• SWT-Bench: Testing and Validating Real-World Bug-Fixes with Code Agents, NeurIPS, 2024

Applications

Requirements Analysis & Software Design

• Demystifying Feature Requests: Leveraging LLMs to Refine Feature Requests in Open-Source Software, Requirements Engineering, 2025
• Clarigen: Bridging instruction gaps via interactive clarification in code generation, AAAI 2025 Workshop on PDLM, 2025
• Agilecoder: Dynamic collaborative agents for software development based on agile methodology, FORGE, 2025
• An exploratory study on architectural smell refactoring using Large Languages Models, IEEE ICSA-C, 2025
• Human-in-the-loop software development agents, ICSE-SEIP, 2025
• Co-PatcheR: Collaborative Software Patching with Component (s)-specific Small Reasoning Models, NeurIPS, 2025
• PACGBI: A Pipeline for Automated Code Generation from Backlog Items, ASE, 2024
• Enhancing automated program repair with solution design, ASE, 2024
• Codes: Natural language to code repository via multi-layer sketch, TOSEM, 2024

Code Generation & Transformation

• Can Long-Context Language Models Solve Repository-Level Code Generation?, LTI Student Research Symposium 2025, 2025
• Mutual-Supervised Learning for Sequential-to-Parallel Code Translation, NeurIPS, 2025
• Assistance or disruption? exploring and evaluating the design and trade-offs of proactive ai programming support, CHI, 2025
• ExploraCoder: Advancing Code Generation for Multiple Unseen APIs via Planning and Chained Exploration, ACL, 2025
• Let's Revise Step-by-Step: A Unified Local Search Framework for Code Generation with LLMs, NeurIPS, 2025
• Agilecoder: Dynamic collaborative agents for software development based on agile methodology, FORGE, 2025
• Execution Guided Line-by-Line Code Generation, NeurIPS, 2025
• Acereason-nemotron: Advancing math and code reasoning through reinforcement learning, NeurIPS, 2025
• Reasoning Through Execution: Unifying Process and Outcome Rewards for Code Generation, ICML, 2025
• Human-in-the-loop software development agents, ICSE-SEIP, 2025
• Maintaincoder: Maintainable code generation under dynamic requirements, NeurIPS, 2025
• AlphaVerus: Bootstrapping Formally Verified Code Generation through Self-Improving Translation and Treefinement, ICML, 2025
• On Simulation-Guided LLM-based Code Generation for Safe Autonomous Driving Software, EASE, 2025
• Improving fim code completions via context & curriculum based learning, WSDM, 2025
• On Pretraining For Project-Level Code Completion, ICLR 2025 Third Workshop on Deep Learning for Code, 2025
• Coding Agents with Multimodal Browsing are Generalist Problem Solvers, ICML 2025 Workshop on Computer Use Agents, 2025
• Test-driven development and llm-based code generation, ASE, 2024
• Codes: Natural language to code repository via multi-layer sketch, TOSEM, 2024
• On coordinating llms and platform knowledge for software modernization and new developments, SSE, 2024
• Evaluating human-ai partnership for llm-based code migration, CHI EA, 2024
• Autocoderover: Autonomous program improvement, Proceedings of the 33rd ACM SIGSOFT International Symposium on Software Testing and Analysis, 2024
• Magis: Llm-based multi-agent framework for github issue resolution, NeurIPS, 2024

Bug Repair & Debugging

• BLAZE: Cross-language and cross-project bug localization via dynamic chunking and hard example learning, TSE, 2025

• AEGIS: An Agent-based Framework for Bug Reproduction from Issue Descriptions, FSE, 2025

• CodeV: Issue Resolving with Visual Data, ACL, 2025

• PATCHAGENT: a practical program repair agent mimicking human expertise, USENIX Conference on Security Symposium, 2025

• SynFix: Dependency-aware program repair via RelationGraph analysis, ACL, 2025

• ChatDBG: Augmenting Debugging with Large Language Models, PACMSE, 2025

• Nemotron-CORTEXA: Enhancing LLM Agents for Software Engineering Tasks via Improved Localization and Solution Diversity, ICML, 2025

• AEGIS: An Agent-based Framework for Bug Reproduction from Issue Descriptions, FSE, 2025

• Improving Automated Issue Resolution via Comprehensive Repository Exploration, ICLR 2025 Third Workshop on Deep Learning for Code, 2025

• MORepair: Teaching LLMs to Repair Code via Multi-Objective Fine-Tuning, TOSEM, 2025

• ROSE: An IDE-Based Interactive Repair Framework for Debugging, TOSEM, 2025

• Autocoderover: Autonomous program improvement, Proceedings of the 33rd ACM SIGSOFT International Symposium on Software Testing and Analysis, 2024

• Magis: Llm-based multi-agent framework for github issue resolution, NeurIPS, 2024

• Swe-agent: Agent-computer interfaces enable automated software engineering, NeurIPS, 2024

Code Refactoring & Quality Improvement

• Artemis ai: Multi-llm framework for code optimisation, CAI, 2025
• Automated High-Level Code Optimization for Warehouse Performance, IEEE Micro, 2025
• Afterburner: Reinforcement learning facilitates self-improving code efficiency optimization, NeurIPS, 2025
• Maintaincoder: Maintainable code generation under dynamic requirements, NeurIPS, 2025
• MACEDON: Supporting Programmers with Real-Time Multi-Dimensional Code Evaluation and Optimization, ACM UIST, 2025
• SWE-GPT: A Process-Centric Language Model for Automated Software Improvement, PACMSE, 2025
• Mutual-Supervised Learning for Sequential-to-Parallel Code Translation, NeurIPS, 2025
• PATCHAGENT: a practical program repair agent mimicking human expertise, USENIX Conference on Security Symposium, 2025
• LLMDFA: analyzing dataflow in code with large language models, NeurIPS, 2024

Testing, Verification, & Security

• You name it, I run it: An LLM agent to execute tests of arbitrary projects, PACMSE, 2025
• Optimizing case-based reasoning system for functional test script generation with large language models, ACM SIGKDD, 2025
• LLM4TDG: test-driven generation of large language models based on enhanced constraint reasoning, Cybersecurity, 2025
• On Simulation-Guided LLM-based Code Generation for Safe Autonomous Driving Software, EASE, 2025
• Synthesizing Performance Constraints for Evaluating and Improving Code Efficiency, NeurIPS, 2025
• Automated Generation of Issue-Reproducing Tests by Combining LLMs and Search-Based Testing, ASE, 2025
• Rethinking Verification for LLM Code Generation: From Generation to Testing, NeurIPS, 2025
• Risk Assessment Framework for Code LLMs via Leveraging Internal States, FSE, 2025
• Teaching an Old LLM Secure Coding: Localized Preference Optimization on Distilled Preferences, ACL, 2025
• AlphaVerus: Bootstrapping Formally Verified Code Generation through Self-Improving Translation and Treefinement, ICML, 2025
• Building A Proof-Oriented Programmer That Is 64% Better Than GPT-4o Under Data Scarcity, ACL, 2025
• Code Graph Model (CGM): A Graph-Integrated Large Language Model for Repository-Level Software Engineering Tasks, NeurIPS, 2025
• Training Language Models to Generate Quality Code with Program Analysis Feedback, NeurIPS, 2025
• PurpCode: Reasoning for Safer Code Generation, NeurIPS, 2025
• PatchPilot: A Cost-Efficient Software Engineering Agent with Early Attempts on Formal Verification, ICML, 2025
• LLMDFA: analyzing dataflow in code with large language models, NeurIPS, 2024

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