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Beijun Shen

22 papers hereh-index 7178 citations25 works total

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

author position
  • middle author11
  • last author10

Across the 21 of 22 papers where every author was matched, so the position is known.

fields
  • cs.SE16
  • cs.CL4
  • cs.AI2
same name
  • Beijun Shen — 5 papers, h 18
  • Beijun Shen — 1 paper
  • Beijun Shen — 1 paper, h 4
  • Beijun Shen — 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
20242026
most citedJust-In-Time Software Defect Prediction via Bi-modal Change Representation Learning

11 citations · 16 across the 20 of their papers we have counts for

collaborators
Showing cs.CLShow all

4 papers · 1 filter

cs.CL2025★ 1 cited

LongCodeZip: Compress Long Context for Code Language Models

Yuling Shi, Yichun Qian, Hongyu Zhang +2

Code generation under long contexts is becoming increasingly critical as Large Language Models (LLMs) are required to reason over extensive information in the codebase. While recen…

cs.CL2025

Transplant Then Regenerate: A New Paradigm for Text Data Augmentation

Guangzhan Wang, Hongyu Zhang, Beijun Shen +1

Data augmentation is a critical technique in deep learning. Traditional methods like Back-translation typically focus on lexical-level rephrasing, which primarily produces variatio…

cs.CL2025

SWE-QA: Can Language Models Answer Repository-level Code Questions?

Weihan Peng, Yuling Shi, Yuhang Wang +3

Understanding and reasoning about entire software repositories is an essential capability for intelligent software engineering tools. While existing benchmarks such as CoSQA and Co…

cs.CL2025

Anti-adversarial Learning: Desensitizing Prompts for Large Language Models

Xuan Li, Zhe Yin, Xiaodong Gu +1

With the widespread use of LLMs, preserving privacy in user prompts has become crucial, as prompts risk exposing privacy and sensitive data to the cloud LLMs. Traditional technique…

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