2 citations · 10 across the 31 of their papers we have counts for
35 papers
SWE-Gate: Passing Functional Tests Is Not Enough for Software Engineering Agents
Xin He, Yanlin Wang, Mingwei Liu +3
Repository-level software engineering benchmarks have significantly advanced the evaluation of coding agents, but existing benchmarks primarily measure whether generated patches pa…
Efficient SWE Agent Benchmarking via Trajectory-Aware Evaluation
Kefeng Duan, Dewu Zheng, Yanlin Wang +7
Evaluating software engineering agents on realistic benchmarks is costly, since each task may require multi-step code exploration, modification, and test execution. Existing effici…
Adaptive Critical Token-Aware Retrieval for Repository-Level Code Generation
Kefeng Duan, Dewu Zheng, Yanlin Wang +8
The repository-level code generation task requires synthesizing code that satisfies task requirements while remaining consistent with the target repository context. Since real-worl…
WebDesignIter: Co-Evolving Design Knowledge for Repository-Level Front-End Code Generation
Zheng Pei, Mingwei Liu, Zhenxi Chen +2
Front-end development accumulates change after change at the repository level, weaving complex cross-file dependencies that current LLM coding agents tuned for single-shot tasks ca…
Bridging Generation and Training: A Systematic Review of Quality Issues in LLMs for Code
Kaifeng He, Xiaojun Zhang, Peiliang Cai +7
Large language models (LLMs) frequently generate defective code, ranging from logical bugs to security vulnerabilities. Training-data quality is one contributor to these outcomes a…
RepoDoc: A Knowledge Graph-Based Framework to Automatic Documentation Generation and Incremental Updates
Dong Xu, Mingwei Liu, Xiwen Wang +2
Maintaining up-to-date, comprehensive documentation for large codebases is a persistent challenge. Recent progress in automated documentation has moved from template-based rules to…
Architecture-Aware Multi-Design Generation for Repository-Level Feature Addition
Mingwei Liu, Zhenxi Chen, Zheng Pei +3
Implementing new features across an entire codebase presents a formidable challenge for Large Language Models (LLMs). This proactive task requires a deep understanding of the globa…
Dynamic analysis enhances issue resolution
Mingwei Liu, Zihao Wang, Zhenxi Chen +3
Resolving complex code defects from natural language descriptions remains a fundamental software engineering challenge. Recently, large language models (LLMs) have driven the creat…
His2Trans: A Knowledge-Guided Agentic Framework for Project-Level C-to-Rust Migration
Shengbo Wang, Mingwei Liu, Guangsheng Ou +3
C remains a major implementation language for operating systems, embedded platforms, and infrastructure software, but manual memory management continues to create security and main…
Unseen-Codebases-Domain Data Synthesis and Training Based on Code Graphs
Guangsheng Ou, Qiming Zhang, Sirong Chen +9
In the context of newly release software frameworks, large language models (LLMs) often exhibit poor performance and a high rate of hallucination, as they are not exposed to such e…
DRAINCODE: Stealthy Energy Consumption Attacks on Retrieval-Augmented Code Generation via Context Poisoning
Yanlin Wang, Jiadong Wu, Tianyue Jiang +7
Large language models (LLMs) have demonstrated impressive capabilities in code generation by leveraging retrieval-augmented generation (RAG) methods. However, the computational cos…
RealSec-bench: A Benchmark for Evaluating Secure Code Generation in Real-World Repositories
Yanlin Wang, Ziyao Zhang, Chong Wang +5
Large Language Models (LLMs) have demonstrated remarkable capabilities in code generation, but their proficiency in producing secure code remains a critical, under-explored area. E…
Advances and Frontiers of LLM-based Issue Resolution in Software Engineering: A Comprehensive Survey
Caihua Li, Lianghong Guo, Yanlin Wang +9
Issue resolution, a complex Software Engineering (SWE) task integral to real-world development, has emerged as a compelling challenge for artificial intelligence. The establishment…
ShortCoder: Knowledge-Augmented Syntax Optimization for Token-Efficient Code Generation
Sicong Liu, Yanxian Huang, Mingwei Liu +6
Code generation tasks aim to automate the conversion of user requirements into executable code, significantly reducing manual development efforts and enhancing software productivit…
SimpleDevQA: Benchmarking Large Language Models on Development Knowledge QA
Jing Zhang, Lianghong Guo, Yanlin Wang +7
The Development Knowledge Question Answering (Dev Knowledge QA) task aims to provide natural language answers to knowledge-seeking questions during software development. To investi…
Knowledge-Graph-Driven Data Synthesis for Low-Resource Software Development: A HarmonyOS Case Study
Mingwei Liu, Zheng Pei, Yanlin Wang +5
In low-resource framework development (e.g., HarmonyOS), large language models (LLMs) often lack sufficient pre-training exposure, resulting in poor code generation performance. Al…
Uncovering Pretraining Code in LLMs: A Syntax-Aware Attribution Approach
Yuanheng Li, Zhuoyang Chen, Xiaoyun Liu +5
As large language models (LLMs) become increasingly capable, concerns over the unauthorized use of copyrighted and licensed content in their training data have grown, especially in…
Knowledge Matters: Injecting Project and Testing Knowledge into LLM-based Unit Test Generation
Anji Li, Mingwei Liu, Zhenxi Chen +5
Automated unit test generation using large language models (LLMs) holds great promise but often struggles with generating tests that are both correct and maintainable in real-world…
EffiReasonTrans: RL-Optimized Reasoning for Code Translation
Yanlin Wang, Rongyi Ou, Yanli Wang +6
Code translation is a crucial task in software development and maintenance. While recent advancements in large language models (LLMs) have improved automated code translation accur…
Generating High-Quality Datasets for Code Editing via Open-Source Language Models
Zekai Zhang, Mingwei Liu, Zhenxi Chen +7
Code editing plays a vital role in software engineering, requiring developers to adjust existing code according to natural language instructions while keeping functionality intact…
EvolMathEval: Towards Evolvable Benchmarks for Mathematical Reasoning via Evolutionary Testing
Shengbo Wang, Mingwei Liu, Zike Li +4
The rapid advancement of Large Language Models (LLMs) poses a significant challenge to existing mathematical reasoning benchmarks. However, these benchmarks tend to become easier o…
A Hierarchical and Evolvable Benchmark for Fine-Grained Code Instruction Following with Multi-Turn Feedback
Guoliang Duan, Mingwei Liu, Yanlin Wang +3
Large language models (LLMs) have advanced significantly in code generation, yet their ability to follow complex programming instructions with layered and diverse constraints remai…
AutoEvoEval: An Automated Framework for Evolving Close-Ended LLM Evaluation Data
JiaRu Wu, Mingwei Liu
Large language models (LLMs) have shown remarkable performance on various tasks, but existing evaluation benchmarks are often static and insufficient to fully assess their robustne…
AdaDec: A Uncertainty-Guided Lookahead Decoding Framework for LLM-Based Code Generation
Kaifeng He, Mingwei Liu, Chong Wang +4
Code generation with large language models (LLMs) is highly sensitive to token selection during decoding, particularly at uncertain decision points that influence program logic. Wh…