21 citations · 21 across the 9 of their papers we have counts for
11 papers
State-Aware Generation of Deployable Websites from Visual Prototypes and Requirements
Ruwei Pan, Junlei Shen, Linhao Wu +7
Generating deployable websites from visual prototypes and textual requirements is increasingly feasible with multimodal models, yet keeping generated projects consistent with their…
Do Modifications Made by Coding Agents Make Subsequent Software Maintenance Harder?
Ruwei Pan, John Zhang, Linhao Wu +3
Coding agents increasingly maintain repositories over time, where each change becomes the starting point for subsequent tasks. Yet existing benchmarks either start tasks from devel…
DeepGuard: Secure Code Generation via Multi-Layer Semantic Aggregation
Li Huang, Zhongxin Liu, Yifan Wu +6
Large Language Models (LLMs) for code generation can replicate insecure patterns from their training data. To mitigate this, a common strategy for security hardening is to fine-tun…
Persistent Cross-Attempt State Optimization for Repository-Level Code Generation
Ruwei Pan, Jiangshuai Wang, Qisheng Zhang +6
Large language models (LLMs) have achieved substantial progress in repository-level code generation. However, solving the same repository-level task often requires multiple attempt…
Toward Executable Repository-Level Code Generation via Environment Alignment
Ruwei Pan, Junlei Shen, Linhao Wu +5
Large language models (LLMs) have achieved strong performance on code generation, but existing methods still struggle with repository-level code generation under executable validat…
Improving Compiler Bug Isolation by Leveraging Large Language Models
Yixian Qi, Jiajun Jiang, Fengjie Li +3
Compilers play a foundational role in building reliable software systems, and bugs within them can lead to catastrophic consequences. The compilation process typically involves hun…
Empirical Evaluation of Large Language Models in Automated Program Repair
Jiajun Sun, Fengjie Li, Xinzhu Qi +2
The increasing prevalence of software bugs has made automated program repair (APR) a key research focus. Large language models (LLMs) offer new opportunities for APR, but existing…
Evaluating the Generalizability of LLMs in Automated Program Repair
Fengjie Li, Jiajun Jiang, Jiajun Sun +1
LLM-based automated program repair methods have attracted significant attention for their state-of-the-art performance. However, they were primarily evaluated on a few well known d…
Hybrid Automated Program Repair by Combining Large Language Models and Program Analysis
Fengjie Li, Jiajun Jiang, Jiajun Sun +1
Automated Program Repair (APR) has garnered significant attention due to its potential to streamline the bug repair process for human developers. Recently, LLM-based APR methods ha…
Boosting Redundancy-based Automated Program Repair by Fine-grained Pattern Mining
Jiajun Jiang, Fengjie Li, Zijie Zhao +5
Redundancy-based automated program repair (APR), which generates patches by referencing existing source code, has gained much attention since they are effective in repairing real-w…
Embedding API Dependency Graph for Neural Code Generation
Chen Lyu, Ruyun Wang, Hongyu Zhang +2
The problem of code generation from textual program descriptions has long been viewed as a grand challenge in software engineering. In recent years, many deep learning based approa…