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Documents authored by Zhang, Ying

Document
Technical Track Paper
DOI: 10.4230/LIPIcs.ESEM.2026.1
CognixShield: PoV-Guided Vulnerable API Usage Detection in Large Codebases via LLMs

Authors: Quanzhi Fu, Wang Lingxiang, Wenjia Song, Gelei Deng, Yi Liu, Dan Williams, and Ying Zhang

Published in: LIPIcs, Volume 394, 20th International Symposium on Empirical Software Engineering and Measurement (ESEM 2026)

Abstract
Background. The integration of open-source libraries in Java development introduces severe security risks through vulnerable APIs. Existing program analysis and deep learning tools face challenges in capturing inter-procedural vulnerability semantics at scale. While LLMs show promise for semantic reasoning, they cannot handle large codebases due to context limits, and they lack the vulnerability-specific understanding needed to determine exploitability. Aim. This work aims to overcome these limitations and enable LLM-based detection of vulnerable API usage in large-scale Java applications. Method. We present CognixShield, an LLM-powered framework for detecting vulnerable API usage through three core components. First, semantic-preserving AST-based fragmentation partitions large codebases while maintaining syntactic completeness within LLM context windows. Second, vulnerability-aware multi-agent RAG traces relevant program context across these fragments, iteratively assembling security-critical context spanning functions and files. Third, PoV-guided semantic reasoning uses Proof-of-Vulnerability tests that encode precise triggering conditions and exploitation mechanics to determine vulnerability. Results. CognixShield achieves 84% precision, 95% recall, 84% accuracy, and an 89% F1-score on 57 real-world Java applications, outperforming state-of-the-art tools. Conclusions. Our results show that vulnerability detection requires specialized architectural innovations beyond generic LLM applications.
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Quanzhi Fu, Wang Lingxiang, Wenjia Song, Gelei Deng, Yi Liu, Dan Williams, and Ying Zhang. CognixShield: PoV-Guided Vulnerable API Usage Detection in Large Codebases via LLMs. In 20th International Symposium on Empirical Software Engineering and Measurement (ESEM 2026). Leibniz International Proceedings in Informatics (LIPIcs), Volume 394, pp. 1:1-1:21, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2026)

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@InProceedings{fu_et_al:LIPIcs.ESEM.2026.1,
  author =	{Fu, Quanzhi and Lingxiang, Wang and Song, Wenjia and Deng, Gelei and Liu, Yi and Williams, Dan and Zhang, Ying},
  title =	{{CognixShield: PoV-Guided Vulnerable API Usage Detection in Large Codebases via LLMs}},
  booktitle =	{20th International Symposium on Empirical Software Engineering and Measurement (ESEM 2026)},
  pages =	{1:1--1:21},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-450-5},
  ISSN =	{1868-8969},
  year =	{2026},
  volume =	{394},
  editor =	{Feldt, Robert and Paasivaara, Maria and Mendez, Daniel and Wagner, Stefan and Bar\'{o}n, Marvin Mu\~{n}oz},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ESEM.2026.1},
  URN =		{urn:nbn:de:0030-drops-279698},
  doi =		{10.4230/LIPIcs.ESEM.2026.1},
  annote =	{Keywords: Vulnerable API usage detection, program analysis, LLMs, agentic RAG}
}
Document
DOI: 10.4230/LIPIcs.GD.2025.11
Towards a Better Understanding of Graph Perception in Immersive Environments

Authors: Lin Zhang, Yao Wang, Ying Zhang, Wilhelm Kerle-Malcharek, Karsten Klein, Falk Schreiber, and Andreas Bulling

Published in: LIPIcs, Volume 357, 33rd International Symposium on Graph Drawing and Network Visualization (GD 2025)

Abstract
As Immersive Analytics (IA) increasingly uses Virtual Reality (VR) for stereoscopic 3D (S3D) graph visualisation, it is crucial to understand how users perceive network structures in these immersive environments. However, little is known about how humans read S3D graphs during task solving, and how gaze behaviour indicates task performance. To address this gap, we report a user study with 18 participants asked to perform three analytical tasks on S3D graph visualisations in a VR environment. Our findings reveal systematic relationships between network structural properties and gaze behaviour. Based on these insights, we contribute a comprehensive eye tracking methodology for analysing human perception in immersive environments and establish eye tracking as a valuable tool for objectively evaluating cognitive load in S3D graph visualisation.
Cite as

Lin Zhang, Yao Wang, Ying Zhang, Wilhelm Kerle-Malcharek, Karsten Klein, Falk Schreiber, and Andreas Bulling. Towards a Better Understanding of Graph Perception in Immersive Environments. In 33rd International Symposium on Graph Drawing and Network Visualization (GD 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 357, pp. 11:1-11:19, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{zhang_et_al:LIPIcs.GD.2025.11,
  author =	{Zhang, Lin and Wang, Yao and Zhang, Ying and Kerle-Malcharek, Wilhelm and Klein, Karsten and Schreiber, Falk and Bulling, Andreas},
  title =	{{Towards a Better Understanding of Graph Perception in Immersive Environments}},
  booktitle =	{33rd International Symposium on Graph Drawing and Network Visualization (GD 2025)},
  pages =	{11:1--11:19},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-403-1},
  ISSN =	{1868-8969},
  year =	{2025},
  volume =	{357},
  editor =	{Dujmovi\'{c}, Vida and Montecchiani, Fabrizio},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.GD.2025.11},
  URN =		{urn:nbn:de:0030-drops-249976},
  doi =		{10.4230/LIPIcs.GD.2025.11},
  annote =	{Keywords: Stereoscopic 3D, Graph Visualisation, Eye Tracking, Graph Perception}
}
Document
Poster Abstract
DOI: 10.4230/LIPIcs.GD.2025.52
Investigating Crossing Perception in 3D Graph Visualisation (Poster Abstract)

Authors: Ying Zhang, Niklas Gröne, Giuseppe Liotta, Falk Schreiber, and Karsten Klein

Published in: LIPIcs, Volume 357, 33rd International Symposium on Graph Drawing and Network Visualization (GD 2025)

Abstract
Human perception and understanding of graph drawings is influenced by a variety of impact factors for which quality measures such as the number of crossings are used as a proxy indicator. For the more and more common stereoscopic 3D (S3D) graph visualisations, evidence is required to better understand graph perception and its relation to quality measures. We investigate the perception of crossing configurations in S3D graph visualisations and present the results of a study.
Cite as

Ying Zhang, Niklas Gröne, Giuseppe Liotta, Falk Schreiber, and Karsten Klein. Investigating Crossing Perception in 3D Graph Visualisation (Poster Abstract). In 33rd International Symposium on Graph Drawing and Network Visualization (GD 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 357, pp. 52:1-52:5, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{zhang_et_al:LIPIcs.GD.2025.52,
  author =	{Zhang, Ying and Gr\"{o}ne, Niklas and Liotta, Giuseppe and Schreiber, Falk and Klein, Karsten},
  title =	{{Investigating Crossing Perception in 3D Graph Visualisation}},
  booktitle =	{33rd International Symposium on Graph Drawing and Network Visualization (GD 2025)},
  pages =	{52:1--52:5},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-403-1},
  ISSN =	{1868-8969},
  year =	{2025},
  volume =	{357},
  editor =	{Dujmovi\'{c}, Vida and Montecchiani, Fabrizio},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.GD.2025.52},
  URN =		{urn:nbn:de:0030-drops-250381},
  doi =		{10.4230/LIPIcs.GD.2025.52},
  annote =	{Keywords: Graph Perception, Stereoscopic 3D Graph Visualisation, Crossing Configurations}
}
Document
Software Abstract
DOI: 10.4230/LIPIcs.GD.2024.57
Immersive Analytics of Graphs in Virtual Reality with GAV-VR (Software Abstract)

Authors: Stefan P. Feyer, Wilhelm Kerle-Malcharek, Ying Zhang, Falk Schreiber, and Karsten Klein

Published in: LIPIcs, Volume 320, 32nd International Symposium on Graph Drawing and Network Visualization (GD 2024)

Abstract
The design space for interactive graph visualisation in immersive environments creates opportunities to improve on established solutions in traditional desktop settings. Exploiting this potential requires careful analysis of achievable benefits, required tradeoffs, and disadvantages for particular designs and use-cases. GAV-VR is a modular and user-extensible framework for graph visualisation and analysis in Virtual Reality (VR). It provides the platform to easily create interactive graph visualisations, facilitating both applied graph analysis and evaluation of approaches and methods for visualisation of and interaction with graphs in VR.
Cite as

Stefan P. Feyer, Wilhelm Kerle-Malcharek, Ying Zhang, Falk Schreiber, and Karsten Klein. Immersive Analytics of Graphs in Virtual Reality with GAV-VR (Software Abstract). In 32nd International Symposium on Graph Drawing and Network Visualization (GD 2024). Leibniz International Proceedings in Informatics (LIPIcs), Volume 320, pp. 57:1-57:3, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)

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@InProceedings{feyer_et_al:LIPIcs.GD.2024.57,
  author =	{Feyer, Stefan P. and Kerle-Malcharek, Wilhelm and Zhang, Ying and Schreiber, Falk and Klein, Karsten},
  title =	{{Immersive Analytics of Graphs in Virtual Reality with GAV-VR}},
  booktitle =	{32nd International Symposium on Graph Drawing and Network Visualization (GD 2024)},
  pages =	{57:1--57:3},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-343-0},
  ISSN =	{1868-8969},
  year =	{2024},
  volume =	{320},
  editor =	{Felsner, Stefan and Klein, Karsten},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.GD.2024.57},
  URN =		{urn:nbn:de:0030-drops-213413},
  doi =		{10.4230/LIPIcs.GD.2024.57},
  annote =	{Keywords: Networks, Immersive Analytics, Software}
}

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