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20242026
most citedBinPRE: Enhancing Field Inference in Binary Analysis Based Protocol Reverse Engineering

1 citations · 1 across the 5 of their papers we have counts for

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cs.SE2026

Assessing the Impact of Requirement Ambiguity on LLM-based Function-Level Code Generation

Di Yang, Xinou Xie, Xiuwen Yang +7

Software requirement ambiguity is ubiquitous in real-world development, stemming from the inherent imprecision of natural language and the varying interpretations of stakeholders.…

cs.SE2026

Improving Random Testing via LLM-powered UI Tarpit Escaping for Mobile Apps

Mengqian Xu, Yiheng Xiong, Le Chang +3

Random GUI testing is a widely-used technique for testing mobile apps. However, its effectiveness is limited by the notorious issue -- UI exploration tarpits, where the exploration…

cs.SE2025

Automated detection of atomicity violations in large-scale systems

Hang He, Yixing Luo, Chengcheng Wan +3

Atomicity violations in interrupt-driven programs pose a significant threat to software reliability in safety-critical systems. These violations occur when the execution sequence o…

cs.SE20241 cited

BinPRE: Enhancing Field Inference in Binary Analysis Based Protocol Reverse Engineering

Jiayi Jiang, Xiyuan Zhang, Chengcheng Wan +3

Protocol reverse engineering (PRE) aims to infer the specification of network protocols when the source code is not available. Specifically, field inference is one crucial step in…

cs.SE2024

Are LLMs Correctly Integrated into Software Systems?

Yuchen Shao, Yuheng Huang, Jiawei Shen +3

Large language models (LLMs) provide effective solutions in various application scenarios, with the support of retrieval-augmented generation (RAG). However, developers face challe…