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researcher

Shuai Lu

7 papers hereh-index 390 citations7 works total

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

author position
  • middle author7

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

fields
  • cs.SE6
  • cs.CL1
same name
  • Shuai Lu — 18 papers, h 14
  • Shuai Lu — 14 papers, h 31
  • Shuai Lu — 14 papers, h 4
  • Shuai Lu — 11 papers, h 15
  • Shuai Lu — 10 papers, h 8
  • Shuai Lu — 9 papers, h 5

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 citedAutoVerus: Automated Proof Generation for Rust Code

10 citations · 10 across the 7 of their papers we have counts for

collaborators
Showing 2026Show all

4 papers · 1 filter

cs.SE2026

From Patches to Trajectories: Privileged Process Supervision for Software-Engineering Agents

Murong Ma, Tianyu Chen, Yun Lin +7

Supervised fine-tuning (SFT) on long teacher trajectories is the dominant way to instill investigation and reasoning in open software-engineering (SWE) agents. Since every retained…

cs.SE2026

Synthesizing File-Level Data for Unit Test Generation with Chain-of-Thoughts via Self-Debugging

Ziyue Hua, Tianyu Chen, Yeyun Gong +8

Automatic unit test (UT) generation is essential for software quality assurance, but existing approaches--including symbolic execution, search-based approaches, and recent LLM-base…

cs.SE2026

Pull Requests as a Training Signal for Repo-Level Code Editing

Qinglin Zhu, Tianyu Chen, Shuai Lu +8

Repository-level code editing requires models to understand complex dependencies and execute precise multi-file modifications across a large codebase. While recent gains on SWE-ben…

cs.SE2026

Reducing the Costs of Proof Synthesis on Rust Systems by Scaling Up a Seed Training Set

Nongyu Di, Tianyu Chen, Shan Lu +6

Large Language Models (LLMs) are widely used for code generation. However, the correctness of code generated by LLMs remains a concern. A potential remedy to this concern is to hav…

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