6 papers
PhoenixRepair: Rethinking Repair Strategy Exploration in Software Agents
Tianyue Jiang, Yanlin Wang, Xin He +7
While Large Language Models have greatly advanced automated issue resolution, existing agent-based methods exhibit a fundamental limitation in their insufficient exploration of rep…
AlignCoder: Aligning Retrieval with Target Intent for Repository-Level Code Completion
Tianyue Jiang, Yanli Wang, Yanlin Wang +5
Repository-level code completion remains a challenging task for existing code large language models (code LLMs) due to their limited understanding of repository-specific context an…
Yet Even Less Is Even Better For Agentic, Reasoning, and Coding LLMs
CodeArts Model Team, Yang Ye, Jingyuan Tan +24
Training effective software engineering agents requires large volumes of task-specific trajectories, incurring substantial data construction costs. Inspired by the "Less-Is-More" h…
DRAINCODE: Stealthy Energy Consumption Attacks on Retrieval-Augmented Code Generation via Context Poisoning
Yanlin Wang, Jiadong Wu, Tianyue Jiang +7
Large language models (LLMs) have demonstrated impressive capabilities in code generation by leveraging retrieval-augmented generation (RAG) methods. However, the computational cos…
Beyond Functional Correctness: Investigating Coding Style Inconsistencies in Large Language Models
Yanlin Wang, Tianyue Jiang, Mingwei Liu +5
Large language models (LLMs) have brought a paradigm shift to the field of code generation, offering the potential to enhance the software development process. However, previous re…
What to Retrieve for Effective Retrieval-Augmented Code Generation? An Empirical Study and Beyond
Wenchao Gu, Juntao Chen, Yanlin Wang +6
Repository-level code generation remains challenging due to complex code dependencies and the limitations of large language models (LLMs) in processing long contexts. While retriev…