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20232025
most citedShow Me Your Code! Kill Code Poisoning: A Lightweight Method Based on Code Naturalness

1 citations · 3 across the 8 of their papers we have counts for

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11 papers · 1 filter

cs.SE2025

VFArchē: A Dual-Mode Framework for Locating Vulnerable Functions in Open-Source Software

Lyuye Zhang, Jian Zhang, Kaixuan Li +6

Software Composition Analysis (SCA) has become pivotal in addressing vulnerabilities inherent in software project dependencies. In particular, reachability analysis is increasingly…

cs.SE20251 cited

Software Development Life Cycle Perspective: A Survey of Benchmarks for Code Large Language Models and Agents

Kaixin Wang, Tianlin Li, Xiaoyu Zhang +7

Code large language models (CodeLLMs) and agents are increasingly being integrated into complex software engineering tasks spanning the entire Software Development Life Cycle (SDLC…

cs.SE2025

Fixing Outside the Box: Uncovering Tactics for Open-Source Security Issue Management

Lyuye Zhang, Jiahui Wu, Chengwei Liu +5

In the rapidly evolving landscape of software development, addressing security vulnerabilities in open-source software (OSS) has become critically important. However, existing rese…

cs.SE20251 cited

Show Me Your Code! Kill Code Poisoning: A Lightweight Method Based on Code Naturalness

Weisong Sun, Yuchen Chen, Mengzhe Yuan +6

Neural code models (NCMs) have demonstrated extraordinary capabilities in code intelligence tasks. Meanwhile, the security of NCMs and NCMs-based systems has garnered increasing at…

cs.SE2024

LLM Based Input Space Partitioning Testing for Library APIs

Jiageng Li, Zhen Dong, Chong Wang +4

Automated library APIs testing is difficult as it requires exploring a vast space of parameter inputs that may involve objects with complex data types. Existing search based approa…

cs.SE2024

Towards Trustworthy LLMs for Code: A Data-Centric Synergistic Auditing Framework

Chong Wang, Zhenpeng Chen, Tianlin Li +2

LLM-powered coding and development assistants have become prevalent to programmers' workflows. However, concerns about the trustworthiness of LLMs for code persist despite their wi…