2 citations · 4 across the 14 of their papers we have counts for
11 papers · 1 filter
JevCoder: When Does Decision Delegation Pay Off in Code Repair?
Jiahao Zhang, Yifan Zhang, Yu Huang
A coding agent repeatedly makes decisions as it repairs code. These decisions shape repair trajectories and affect the token cost of successive model requests. We ask which repair…
GraphAlignCoder: Aligning Program and Proof Graphs for Code Generation
Yueke Zhang, Zihan Fang, Kevin Leach +1
Code large language models (LLMs) can generate syntactically plausible programs that nevertheless violate hidden semantic constraints. Existing execution-feedback training methods…
Coupling Planning with Episodic Memory in LLM Agents for Software Issue Resolution
Jiahao Zhang, Yifan Zhang, Yu Huang
Resolving a real software issue with a large language model (LLM) agent is a long repair episode, often tens to hundreds of steps spanning exploration, hypothesis, implementation,…
SCOPE: Leveraging Subgoal Critiques for Code Generation
Yueke Zhang, Yifan Zhang, Zihan Fang +3
Code generation with large language models (LLMs) remains unreliable because generated programs can appear correct while still violating key semantic requirements in the natural la…
SynthFix: Adaptive Neuro-Symbolic Code Vulnerability Repair
Yifan Zhang, Jieyu Li, Kexin Pei +2
Large Language Models (LLMs) can generate plausible code patches, but plausibility is not enough for automated repair: a patch must compile, pass tests, and remove the target vulne…
EyeLayer: Integrating Human Attention Patterns into LLM-Based Code Summarization
Jiahao Zhang, Yifan Zhang, Kevin Leach +1
Code summarization is the task of generating natural language descriptions of source code, which is critical for software comprehension and maintenance. While large language models…
DPO-F+: Aligning Code Repair Feedback with Developers' Preferences
Zihan Fang, Yifan Zhang, Yueke Zhang +2
Large Language Models (LLMs) are increasingly used in software engineering tasks, especially code repair. However, developers often struggle to interpret model outputs, limiting ef…
CodeGrad: Integrating Multi-Step Verification with Gradient-Based LLM Refinement
Yueke Zhang, Yifan Zhang, Kevin Leach +1
While Large Language Models (LLMs) have demonstrated remarkable capabilities in code generation, they often produce solutions that lack guarantees of correctness, robustness, and e…
Enhancing Code LLM Training with Programmer Attention
Yifan Zhang, Chen Huang, Zachary Karas +3
Human attention provides valuable yet underexploited signals for code LLM training, offering a perspective beyond purely machine-driven attention. Despite the complexity and cost o…
Optimizing Code Runtime Performance through Context-Aware Retrieval-Augmented Generation
Manish Acharya, Yifan Zhang, Kevin Leach +1
Optimizing software performance through automated code refinement offers a promising avenue for enhancing execution speed and efficiency. Despite recent advancements in LLMs, a sig…
COMCAT: Leveraging Human Judgment to Improve Automatic Documentation and Summarization
Skyler Grandel, Scott Thomas Andersen, Yu Huang +1
Software maintenance constitutes a substantial portion of the total lifetime costs of software, with a significant portion attributed to code comprehension. Software comprehension…