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cs.SE2026
An Empirical Study of Proactive Coding Assistants in Real-World Software Development
Lehui Li, Ruixuan Jia, Guo-Ye Yang +1
Large language model (LLM)-based coding assistants have made substantial progress, yet most systems remain reactive, requiring developers to explicitly formulate their needs. Proac…
cs.SE2026
DebugRepair: Enhancing LLM-Based Automated Program Repair via Self-Directed Debugging
Linhao Wu, Yifei Pei, Zhen Yang +9
Automated Program Repair (APR) has benefited from the code understanding and generation capabilities of Large Language Models (LLMs). Existing feedback-based APR methods iterativel…
cs.SE2024★ 1 cited
ClassEval-T: Evaluating Large Language Models in Class-Level Code Translation
Pengyu Xue, Linhao Wu, Zhen Yang +9
In recent years, Large Language Models (LLMs) have dramatically advanced the performance of automated code translation, making their computational accuracy score reach up to over 8…