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Chongyang Tao

7 papers hereh-index 5129 citations9 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • middle author2
  • last author1

Across the 3 of 7 papers where every author was matched, so the position is known.

fields
  • cs.SE4
  • cs.CL2
  • cs.AI1
same name
  • Chongyang Tao — 53 papers, h 35
  • Chongyang Tao — 14 papers, h 1
  • Chongyang Tao — 4 papers, h 6
  • Chongyang Tao — 1 paper

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20232026
most citedLarge Language Model-Aware In-Context Learning for Code Generation

7 citations · 11 across the 7 of their papers we have counts for

collaborators
Showing cs.SEShow all

4 papers · 1 filter

cs.SE2026

CodeMEM: AST-Guided Adaptive Memory for Repository-Level Iterative Code Generation

Peiding Wang, Li Zhang, Fang Liu +2

Large language models (LLMs) substantially enhance developer productivity in repository-level code generation through interactive collaboration. However, as interactions progress,…

cs.SE2025★ 2 cited

GraphCodeAgent: Dual Graph-Guided LLM Agent for Retrieval-Augmented Repo-Level Code Generation

Jia Li, Xianjie Shi, Kechi Zhang +10

Writing code requires significant time and effort in software development. To automate this process, researchers have made substantial progress for code generation. Recently, large…

cs.SE2025★ 1 cited

LONGCODEU: Benchmarking Long-Context Language Models on Long Code Understanding

Jia Li, Xuyuan Guo, Lei Li +7

Current advanced long-context language models offer great potential for real-world software engineering applications. However, progress in this critical domain remains hampered by…

cs.SE2023★ 7 cited

Large Language Model-Aware In-Context Learning for Code Generation

Jia Li, Ge Li, Chongyang Tao +3

Large language models (LLMs) have shown impressive in-context learning (ICL) ability in code generation. LLMs take a prompt consisting of requirement-code examples and a new requir…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.