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Chao Shen

4 papers here

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

author position
  • middle author1
  • last author2

Across the 3 of 4 papers where every author was matched, so the position is known.

fields
  • cs.SE3
  • cs.CL1
ORCID 0000-0001-5483-3348
same name
  • Chao Shen — 16 papers, h 23
  • Chao Shen — 10 papers
  • Chao Shen — 7 papers
  • Chao Shen — 7 papers
  • Chao Shen — 5 papers
  • Chao Shen — 4 papers, h 24

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

most citedYour Large Language Model is Secretly a Fairness Proponent and You Should Prompt it Like One

3 citations · 3 across the 4 of their papers we have counts for

collaborators

4 papers

cs.SE2025

AutoEmpirical: LLM-Based Automated Research for Empirical Software Fault Analysis

Jiongchi Yu, Weipeng Jiang, Xiaoyu Zhang +3

Understanding software faults is essential for empirical research in software development and maintenance. However, traditional fault analysis, while valuable, typically involves m…

cs.SE2025

Benchmarking and Revisiting Code Generation Assessment: A Mutation-Based Approach

Longtian Wang, Tianlin Li, Xiaofei Xie +3

Code Large Language Models (CLLMs) have exhibited outstanding performance in program synthesis, attracting the focus of the research community. The evaluation of CLLM's program syn…

cs.SE2024

Efficient DNN-Powered Software with Fair Sparse Models

Xuanqi Gao, Weipeng Jiang, Juan Zhai +3

With the emergence of the Software 3.0 era, there is a growing trend of compressing and integrating large models into software systems, with significant societal implications. Regr…

cs.CL2024★ 3 cited

Your Large Language Model is Secretly a Fairness Proponent and You Should Prompt it Like One

Tianlin Li, Xiaoyu Zhang, Chao Du +5

The widespread adoption of large language models (LLMs) underscores the urgent need to ensure their fairness. However, LLMs frequently present dominant viewpoints while ignoring al…

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