7 citations · 11 across the 9 of their papers we have counts for
8 papers · 1 filter
CodeRL+: Improving Code Generation via Reinforcement with Execution Semantics Alignment
Xue Jiang, Yihong Dong, Mengyang Liu +10
While Large Language Models (LLMs) excel at code generation by learning from vast code corpora, a fundamental semantic gap remains between their training on textual patterns and th…
Large Language Model Unlearning for Source Code
Xue Jiang, Yihong Dong, Huangzhao Zhang +9
While Large Language Models (LLMs) excel at code generation, their inherent tendency toward verbatim memorization of training data introduces critical risks like copyright infringe…
Thinking Longer, Not Larger: Enhancing Software Engineering Agents via Scaling Test-Time Compute
Yingwei Ma, Yongbin Li, Yihong Dong +5
Recent advancements in software engineering agents have demonstrated promising capabilities in automating program improvements. However, their reliance on closed-source or resource…
Do Code LLMs Understand Design Patterns?
Zhenyu Pan, Xuefeng Song, Yunkun Wang +4
Code Large Language Models (LLMs) demonstrate great versatility in adapting to various downstream tasks, including code generation and completion, as well as bug detection and fixi…
LLMs as Continuous Learners: Improving the Reproduction of Defective Code in Software Issues
Yalan Lin, Yingwei Ma, Rongyu Cao +4
Reproducing buggy code is the first and crucially important step in issue resolving, as it aids in identifying the underlying problems and validating that generated patches resolve…
Lingma SWE-GPT: An Open Development-Process-Centric Language Model for Automated Software Improvement
Yingwei Ma, Rongyu Cao, Yongchang Cao +7
Recent advancements in LLM-based agents have led to significant progress in automatic software engineering, particularly in software maintenance and evolution. Despite these encour…