7 citations · 11 across the 9 of their papers we have counts for
6 papers · 1 filter
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…
In-Context Transfer Learning: Demonstration Synthesis by Transferring Similar Tasks
Dingzirui Wang, Xuanliang Zhang, Qiguang Chen +9
In-context learning (ICL) is an effective approach to help large language models (LLMs) adapt to various tasks by providing demonstrations of the target task. Considering the high…
Codev-Bench: How Do LLMs Understand Developer-Centric Code Completion?
Zhenyu Pan, Rongyu Cao, Yongchang Cao +5
Code completion, a key downstream task in code generation, is one of the most frequent and impactful methods for enhancing developer productivity in software development. As intell…
Scaling Offline Model-Based RL via Jointly-Optimized World-Action Model Pretraining
Jie Cheng, Ruixi Qiao, Yingwei Ma +5
A significant aspiration of offline reinforcement learning (RL) is to develop a generalist agent with high capabilities from large and heterogeneous datasets. However, prior approa…
Alibaba LingmaAgent: Improving Automated Issue Resolution via Comprehensive Repository Exploration
Yingwei Ma, Qingping Yang, Rongyu Cao +3
This paper presents Alibaba LingmaAgent, a novel Automated Software Engineering method designed to comprehensively understand and utilize whole software repositories for issue reso…