16 citations · 18 across the 29 of their papers we have counts for
51 papers · 1 filter
Software Engineering for and with GUI Agent
Shengcheng Yu, Yuchen Ling, Junyang Xing +3
GUI agents have advanced rapidly, producing a growing body of frameworks, benchmarks, and applications. However, this growth has outpaced the maturity of the field. GUI agents rema…
ReProAgent: Tool-Augmented Multi-Stage Agentic Generation of Bug Reproduction Tests from Issue Reports
Quanjun Zhang, Yi Zheng, Ye Shang +5
Reproduction tests help developers confirm reported issues and provide executable feedback for issue resolution, yet issue reports in open-source projects rarely include such tests…
Multi-Agent LLM Collaboration for Unit Test Generation via Human-Testing-Inspired Workflows
Quanjun Zhang, Ye Shang, Siqi Gu +4
Recently, the emergence of Large Language Models (LLMs) has spurred a surge of research into automated unit test generation, yielding impressive performance and reducing manual eff…
Rise From The Ashes: LLM-based Static Analysis for Deep Learning Framework Bugs
Shaoyu Yang, Haifeng Lin, Chunrong Fang +6
Deep learning (DL) frameworks are critical AI infrastructures that often hide bugs with serious security implications. While dynamic approaches such as fuzzing are effective in unc…
Investigating Metamorphic Fuzz Oracle Enhancement via Large Language Models
Ruixiang Qian, Ding Yang, Zengxu Chen +4
Fuzz drivers are essential components of greybox fuzzing, as they encapsulate target interfaces, define test spaces, and largely determine fuzzing effectiveness. Existing fuzz driv…
EvoRepair: Enhancing Vulnerability Repair Agents Through Experience-Based Self-Evolution
Haichuan Hu, Guoqing Xie, Quanjun Zhang +5
Large Language Models (LLMs) have shown promise for automated vulnerability repair (AVR), but they still face several limitations, including the lack of intra-vulnerability experie…