5 citations · 9 across the 22 of their papers we have counts for
23 papers
MRCoder: An Efficient Context Selecting Approach for Repository-Level Code Generation
Peiding Wang, Li Zhang, Fang Liu
Large language models (LLMs) have demonstrated strong capabilities in code generation. However, repository-level code generation remains challenging, as it requires effectively ide…
CodeSpec: Dual Executable Specifications for Agentic Long-Horizon Feature Development
Peiding Wang, Li Zhang, Fang Liu +2
LLM-based code agents have advanced repository-level software development through iterative interaction with codebases and tools. However, feature development requires integrating…
ContraFix: Skill-Enhanced Contrastive Runtime Analysis for Vulnerability Repair
Simiao Liu, Fang Liu, Peiding Wang +4
As software systems grow increasingly complex, automated vulnerability repair (AVR) remains difficult because the materials available to a repair system are usually failure artifac…
MemRepair: Hierarchical Memory for Agentic Repository-Level Vulnerability Repair
Simiao Liu, Li Zhang, Fang Liu +3
Modern software ecosystems face a rapidly growing number of disclosed vulnerabilities, increasing the need for automated repair techniques that can operate reliably at repository s…
Benchmarking Requirement-to-Architecture Generation with Hybrid Evaluation
Minxiao Li, Li Zhang, Shuying Yan +4
Software architecture serves as the blueprint of a software system, capturing high-level structural decisions that shape downstream implementation and system quality. Despite this…
An Empirical Study of Interaction Smells in Multi-Turn Human-LLM Collaborative Code Generation
Binquan Zhang, Li Zhang, Lin Shi +6
Large Language Models (LLMs) have revolutionized code generation, evolving from static tools into dynamic conversational interfaces that facilitate complex, multi-turn collaborativ…
A Scalable Benchmark for Repository-Oriented Long-Horizon Conversational Context Management
Yang Liu, Li Zhang, Fang Liu +2
In recent years, large language models (LLMs) have advanced rapidly, substantially enhancing their code understanding and generation capabilities and giving rise to powerful code a…
CangjieBench: Benchmarking LLMs on a Low-Resource General-Purpose Programming Language
Junhang Cheng, Fang Liu, Jia Li +3
Large Language Models excel in high-resource programming languages but struggle with low-resource ones. Existing research related to low-resource programming languages primarily fo…
CodeMEM: AST-Guided Adaptive Memory for Repository-Level Iterative Code Generation
Peiding Wang, Li Zhang, Fang Liu +2
Large language models (LLMs) substantially enhance developer productivity in repository-level code generation through interactive collaboration. However, as interactions progress,…
Aligning Academia with Industry: An Empirical Study of Industrial Needs and Academic Capabilities in AI-Driven Software Engineering
Hang Yu, Yuzhou Lai, Li Zhang +6
The rapid advancement of large language models (LLMs) is fundamentally reshaping software engineering (SE), driving a paradigm shift in both academic research and industrial practi…
Towards Realistic Project-Level Code Generation via Multi-Agent Collaboration and Semantic Architecture Modeling
Qianhui Zhao, Li Zhang, Fang Liu +8
In recent years, Large Language Models (LLMs) have achieved remarkable progress in automated code generation. In real-world software engineering, the growing demand for rapid itera…
SecureReviewer: Enhancing Large Language Models for Secure Code Review through Secure-aware Fine-tuning
Fang Liu, Simiao Liu, Yinghao Zhu +2
Identifying and addressing security issues during the early phase of the development lifecycle is critical for mitigating the long-term negative impacts on software systems. Code r…
An Empirical Study on Failures in Automated Issue Solving
Simiao Liu, Fang Liu, Liehao Li +4
Automated issue solving seeks to autonomously identify and repair defective code snippets across an entire codebase. SWE-Bench has emerged as the most widely adopted benchmark for…
Are They All Good? Evaluating the Quality of CoTs in LLM-based Code Generation
Binquan Zhang, Li Zhang, Zhiwen Luo +4
Large language models (LLMs) have demonstrated impressive performance in code generation, particularly when augmented with chain-of-thought (CoT) prompting techniques. They break d…
AdaptiveLLM: A Framework for Selecting Optimal Cost-Efficient LLM for Code-Generation Based on CoT Length
Junhang Cheng, Fang Liu, Chengru Wu +1
While Large Language Models (LLMs) have significantly advanced code generation efficiency, they face inherent challenges in balancing performance and inference costs across diverse…
Incorporating Verification Standards for Security Requirements Generation from Functional Specifications
Xiaoli Lian, Shuaisong Wang, Hanyu Zou +3
In the current software driven era, ensuring privacy and security is critical. Despite this, the specification of security requirements for software is still largely a manual and l…
CodeIF-Bench: Evaluating Instruction-Following Capabilities of Large Language Models in Interactive Code Generation
Peiding Wang, Li Zhang, Fang Liu +4
Large Language Models (LLMs) have demonstrated exceptional performance in code generation tasks and have become indispensable programming assistants for developers. However, existi…
FastCoder: Accelerating Repository-level Code Generation via Efficient Retrieval and Verification
Qianhui Zhao, Li Zhang, Fang Liu +6
Code generation is a latency-sensitive task that demands high timeliness. However, with the growing interest and inherent difficulty in repository-level code generation, most exist…
AutoPLC: Generating Vendor-Aware Structured Text for Programmable Logic Controllers
Donghao Yang, Aolang Wu, Tianyi Zhang +6
Among the programming languages for Programmable Logic Controllers (PLCs), Structured Text (ST) is widely adopted for industrial automation due to its expressiveness and flexibilit…
FastFixer: An Efficient and Effective Approach for Repairing Programming Assignments
Fang Liu, Zhenwei Liu, Qianhui Zhao +6
Providing personalized and timely feedback for student's programming assignments is useful for programming education. Automated program repair (APR) techniques have been used to fi…
Enhancing Automated Program Repair with Solution Design
Jiuang Zhao, Donghao Yang, Li Zhang +3
Automatic Program Repair (APR) endeavors to autonomously rectify issues within specific projects, which generally encompasses three categories of tasks: bug resolution, new feature…
Uncovering Weaknesses in Neural Code Generation
Xiaoli Lian, Shuaisong Wang, Jieping Ma +5
Code generation, the task of producing source code from prompts, has seen significant advancements with the advent of pre-trained large language models (PLMs). Despite these achiev…
Challenges of Using Pre-trained Models: the Practitioners' Perspective
Xin Tan, Taichuan Li, Ruohe Chen +2
The challenges associated with using pre-trained models (PTMs) have not been specifically investigated, which hampers their effective utilization. To address this knowledge gap, we…