11 citations · 15 across the 12 of their papers we have counts for
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cs.SE2025
Benchmarking and Studying the LLM-based Agent System in End-to-End Software Development
Zhengran Zeng, Yixin Li, Rui Xie +2
The development of LLM-based autonomous agents for end-to-end software development represents a significant paradigm shift in software engineering. However, the scientific evaluati…
cs.SE2025
SWR-Bench: Assessing LLM Performance in Real-World Code Review Comment Generation
Zhengran Zeng, Ruikai Shi, Keke Han +7
Automated Code Review (ACR) is crucial for software quality, yet existing benchmarks often fail to reflect real-world complexities, hindering the evaluation of modern Large Languag…
cs.SE2025★ 1 cited
Seed&Steer: Guiding Large Language Models with Compilable Prefix and Branch Signals for Unit Test Generation
Shuaiyu Zhou, Zhengran Zeng, Xiaoling Zhou +3
Unit tests play a vital role in the software development lifecycle. Recent advances in Large Language Model (LLM)-based approaches have significantly improved automated test genera…