research-article

Graph Neural Networks for Vulnerability Detection: A Counterfactual Explanation

Authors:

Hai JinAuthors Info & Claims

ISSTA 2024: Proceedings of the 33rd ACM SIGSOFT International Symposium on Software Testing and Analysis

Pages 389 - 401

https://doi.org/10.1145/3650212.3652136

Published: 11 September 2024 Publication History

Abstract

Vulnerability detection is crucial for ensuring the security and reliability of software systems. Recently, Graph Neural Networks (GNNs) have emerged as a prominent code embedding approach for vulnerability detection, owing to their ability to capture the underlying semantic structure of source code. However, GNNs face significant challenges in explainability due to their inherently black-box nature. To this end, several factual reasoning-based explainers have been proposed. These explainers provide explanations for the predictions made by GNNs by analyzing the key features that contribute to the outcomes. We argue that these factual reasoning-based explanations cannot answer critical what-if questions: "What would happen to the GNN's decision if we were to alter the code graph into alternative structures?" Inspired by advancements of counterfactual reasoning in artificial intelligence, we propose CFExplainer, a novel counterfactual explainer for GNN-based vulnerability detection. Unlike factual reasoning-based explainers, CFExplainer seeks the minimal perturbation to the input code graph that leads to a change in the prediction, thereby addressing the what-if questions for vulnerability detection. We term this perturbation a counterfactual explanation, which can pinpoint the root causes of the detected vulnerability and furnish valuable insights for developers to undertake appropriate actions for fixing the vulnerability. Extensive experiments on four GNN-based vulnerability detection models demonstrate the effectiveness of CFExplainer over existing state-of-the-art factual reasoning-based explainers.

References

[1]

2021. Facebook Infer: a tool to detect bugs in Java and C/C++/Objective-C code. https://fbinfer.com/

[2]

2021. Joern - The Bug Hunter’s Workbench. https://joern.io/

[3]

Carlo Abrate and Francesco Bonchi. 2021. Counterfactual Graphs for Explainable Classification of Brain Networks. In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining (KDD ’21). Association for Computing Machinery, New York, NY, USA. 2495–2504. isbn:9781450383325

Digital Library

[4]

Mohit Bajaj, Lingyang Chu, Zi Yu Xue, Jian Pei, Lanjun Wang, Peter Cho-Ho Lam, and Yong Zhang. 2021. Robust Counterfactual Explanations on Graph Neural Networks. In Proceedings of Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, NeurIPS 2021, December 6-14, 2021, virtual. 5644–5655.

[5]

Nghi D. Q. Bui, Yijun Yu, and Lingxiao Jiang. 2019. AutoFocus: Interpreting Attention-Based Neural Networks by Code Perturbation. In Proceedings of the 34th IEEE/ACM International Conference on Automated Software Engineering (ASE). 38–41.

Digital Library

[6]

Saikat Chakraborty, Rahul Krishna, Yangruibo Ding, and Baishakhi Ray. 2022. Deep Learning Based Vulnerability Detection: Are We There Yet? IEEE Transactions on Software Engineering, 48, 9 (2022), 3280–3296.

[7]

Xiao Cheng, Haoyu Wang, Jiayi Hua, Guoai Xu, and Yulei Sui. 2021. DeepWukong: Statically Detecting Software Vulnerabilities Using Deep Graph Neural Network. ACM Trans. Softw. Eng. Methodol., 30, 3 (2021), Article 38, apr, 33 pages. issn:1049-331X

Digital Library

[8]

Kyunghyun Cho, Bart van Merriënboer, Caglar Gulcehre, Dzmitry Bahdanau, Fethi Bougares, Holger Schwenk, and Yoshua Bengio. 2014. Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation. In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Doha, Qatar. 1724–1734.

[9]

Jürgen Cito, Isil Dillig, Seohyun Kim, Vijayaraghavan Murali, and Satish Chandra. 2021. Explaining Mispredictions of Machine Learning Models Using Rule Induction. In Proceedings of the 29th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE 2021). Association for Computing Machinery, New York, NY, USA. 716–727. isbn:9781450385626

Digital Library

[10]

Jürgen Cito, Isil Dillig, Vijayaraghavan Murali, and Satish Chandra. 2022. Counterfactual Explanations for Models of Code. In Proceedings of the 44th International Conference on Software Engineering: Software Engineering in Practice (ICSE-SEIP ’22). Association for Computing Machinery, New York, NY, USA. 125–134. isbn:9781450392266

Digital Library

[11]

Higor A. de Souza, Marcos L. Chaim, and Fabio Kon. 2016. Spectrum-based software fault localization: A survey of techniques, advances, and challenges. arXiv preprint arXiv:1607.04347.

[12]

Yangruibo Ding, Sahil Suneja, Yunhui Zheng, Jim Laredo, Alessandro Morari, Gail Kaiser, and Baishakhi Ray. 2022. VELVET: a noVel Ensemble Learning approach to automatically locate VulnErable sTatements. In Proceedings of 2022 IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER). 959–970.

[13]

Jiahao Fan, Yi Li, Shaohua Wang, and Tien N. Nguyen. 2020. A C/C++ Code Vulnerability Dataset with Code Changes and CVE Summaries. In Proceedings of the 17th International Conference on Mining Software Repositories (MSR ’20). Association for Computing Machinery, New York, NY, USA. 508–512. isbn:9781450375177

[14]

Michael Fu and Chakkrit Tantithamthavorn. 2022. LineVul: A Transformer-based Line-Level Vulnerability Prediction. In Proceedings of 2022 IEEE/ACM 19th International Conference on Mining Software Repositories (MSR). 608–620.

Digital Library

[15]

Tom Ganz, Martin Härterich, Alexander Warnecke, and Konrad Rieck. 2021. Explaining Graph Neural Networks for Vulnerability Discovery. In Proceedings of the 14th ACM Workshop on Artificial Intelligence and Security (AISec ’21). Association for Computing Machinery, New York, NY, USA. 145–156. isbn:9781450386579

Digital Library

[16]

Qing Gao, Sen Ma, Sihao Shao, Yulei Sui, Guoliang Zhao, Luyao Ma, Xiao Ma, Fuyao Duan, Xiao Deng, Shikun Zhang, and Xianglong Chen. 2018. CoBOT: Static C/C++ Bug Detection in the Presence of Incomplete Code. In Proceedings of the 26th IEEE/ACM International Conference on Program Comprehension (ICPC). 385–3853.

Digital Library

[17]

Madelyn Glymour, Judea Pearl, and Nicholas P. Jewell. 2016. Causal Inference in Statistics: A Primer. John Wiley & Sons.

[18]

Mianxue Gu, Hantao Feng, Hongyu Sun, Peng Liu, Qiuling Yue, Jinglu Hu, Chunjie Cao, and Yuqing Zhang. 2022. Hierarchical Attention Network for Interpretable and Fine-Grained Vulnerability Detection. In Proceedings of the IEEE INFOCOM 2022 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS). 1–6.

[19]

Daya Guo, Shuo Ren, Shuai Lu, Zhangyin Feng, Duyu Tang, Shujie Liu, Long Zhou, Nan Duan, Alexey Svyatkovskiy, Shengyu Fu, Michele Tufano, Shao Kun Deng, Colin B. Clement, Dawn Drain, Neel Sundaresan, Jian Yin, Daxin Jiang, and Ming Zhou. 2021. GraphCodeBERT: Pre-training Code Representations with Data Flow. In Proceedings of the 9th International Conference on Learning Representations, ICLR 2021, Virtual Event, Austria, May 3-7, 2021.

[20]

David Hin, Andrey Kan, Huaming Chen, and M. Ali Babar. 2022. LineVD: Statement-Level Vulnerability Detection Using Graph Neural Networks. In Proceedings of the 19th International Conference on Mining Software Repositories (MSR ’22). Association for Computing Machinery, New York, NY, USA. 596–607. isbn:9781450393034

[21]

Yutao Hu, Suyuan Wang, Wenke Li, Junru Peng, Yueming Wu, Deqing Zou, and Hai Jin. 2023. Interpreters for GNN-Based Vulnerability Detection: Are We There Yet? In Proceedings of the 32nd International Symposium on Software Testing and Analysis, ISSTA 2023, Seattle, Washington, United States, July 18-20, 2023.

Digital Library

[22]

Zexi Huang, Mert Kosan, Sourav Medya, Sayan Ranu, and Ambuj Singh. 2023. Global Counterfactual Explainer for Graph Neural Networks. In Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining (WSDM ’23). Association for Computing Machinery, New York, NY, USA. 141–149. isbn:9781450394079

Digital Library

[23]

Fabian Keller, Lars Grunske, Simon Heiden, Antonio Filieri, Andre van Hoorn, and David Lo. 2017. A Critical Evaluation of Spectrum-Based Fault Localization Techniques on a Large-Scale Software System. In Proceedings of 2017 IEEE International Conference on Software Quality, Reliability and Security (QRS). 114–125.

[24]

Diederick P. Kingma and Jimmy Ba. 2015. Adam: A method for stochastic optimization. In Proceedings of the International Conference on Learning Representations (ICLR).

[25]

Thomas N. Kipf and Max Welling. 2017. Semi-Supervised Classification with Graph Convolutional Networks. In Proceedings of the 5th International Conference on Learning Representations, ICLR 2017, Toulon, France, April 24-26, 2017, Conference Track Proceedings. OpenReview.net.

[26]

Qian Li, Xiangmeng Wang, Zhichao Wang, and Guandong Xu. 2023. Be causal: De-biasing social network confounding in recommendation. ACM Transactions on Knowledge Discovery from Data, 17, 1 (2023), 1–23.

[27]

Qian Li, Zhichao Wang, Shaowu Liu, Gang Li, and Guandong Xu. 2021. Causal optimal transport for treatment effect estimation. IEEE transactions on neural networks and learning systems, 34, 8 (2021), 4083–4095.

[28]

Yujia Li, Daniel Tarlow, Marc Brockschmidt, and Richard S. Zemel. 2016. Gated Graph Sequence Neural Networks. In Proceedings of the 4th International Conference on Learning Representations, ICLR 2016, San Juan, Puerto Rico, May 2-4, 2016, Conference Track Proceedings.

[29]

Yi Li, Shaohua Wang, and Tien N. Nguyen. 2021. Vulnerability Detection with Fine-Grained Interpretations. In Proceedings of the 29th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE 2021). Association for Computing Machinery, New York, NY, USA. 292–303. isbn:9781450385626

[30]

Zhen Li, Deqing Zou, Shouhuai Xu, Zhaoxuan Chen, Yawei Zhu, and Hai Jin. 2022. VulDeeLocator: A Deep Learning-Based Fine-Grained Vulnerability Detector. IEEE Transactions on Dependable and Secure Computing, 19, 4 (2022), 2821–2837.

[31]

Zhen Li, Deqing Zou, Shouhuai Xu, Xinyu Ou, Hai Jin, Sujuan Wang, Zhijun Deng, and Yuyi Zhong. 2018. VulDeePecker: A Deep Learning-Based System for Vulnerability Detection. In Proceedings of the 25th Annual Network and Distributed System Security Symposium, NDSS 2018, San Diego, California, USA, February 18-21, 2018. The Internet Society.

[32]

Wanyu Lin, Hao Lan, and Baochun Li. 2021. Generative causal explanations for graph neural networks. In Proceedings of the International Conference on Machine Learning. 6666–6679.

[33]

Ana Lucic, Maartje A. Ter Hoeve, Gabriele Tolomei, Maarten De Rijke, and Fabrizio Silvestri. 2022. CF-GNNExplainer: Counterfactual Explanations for Graph Neural Networks. In Proceedings of The 25th International Conference on Artificial Intelligence and Statistics (Proceedings of Machine Learning Research, Vol. 151). PMLR, 4499–4511.

[34]

Dongsheng Luo, Wei Cheng, Dongkuan Xu, Wenchao Yu, Bo Zong, Haifeng Chen, and Xiang Zhang. 2020. Parameterized Explainer for Graph Neural Network. In Proceedings of the 34th International Conference on Neural Information Processing Systems (NIPS’20). Curran Associates Inc., Red Hook, NY, USA. Article 1646, 12 pages. isbn:9781713829546

Digital Library

[35]

Jing Ma, Ruocheng Guo, Saumitra Mishra, Aidong Zhang, and Jundong Li. 2022. CLEAR: Generative Counterfactual Explanations on Graphs. In Proceedings of the Advances in Neural Information Processing Systems.

[36]

Christopher Morris, Martin Ritzert, Matthias Fey, William L. Hamilton, Jan Eric Lenssen, Gaurav Rattan, and Martin Grohe. 2019. Weisfeiler and Leman Go Neural: Higher-Order Graph Neural Networks. In Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence and Thirty-First Innovative Applications of Artificial Intelligence Conference and Ninth AAAI Symposium on Educational Advances in Artificial Intelligence (AAAI’19/IAAI’19/EAAI’19). AAAI Press, Article 565, 8 pages. isbn:978-1-57735-809-1

Digital Library

[37]

Danilo Numeroso and Davide Bacciu. 2021. Meg: Generating molecular counterfactual explanations for deep graph networks. In Proceedings of 2021 International Joint Conference on Neural Networks (IJCNN). 1–8.

[38]

Md Rafiqul Islam Rabin, Vincent J. Hellendoorn, and Mohammad Amin Alipour. 2021. Understanding Neural Code Intelligence through Program Simplification. In Proceedings of the 29th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE 2021). Association for Computing Machinery, New York, NY, USA. 441–452. isbn:9781450385626

[39]

Neal J. Roese. 1997. Counterfactual thinking. Psychological Bulletin, 121, 1 (1997), 133.

[40]

Thomas Schnake, Oliver Eberle, Jonas Lederer, Shinichi Nakajima, Kristof T. Schütt, Klaus-Robert Müller, and Grégoire Montavon. 2022. Higher-Order Explanations of Graph Neural Networks via Relevant Walks. IEEE Transactions on Pattern Analysis and Machine Intelligence, 44, 11 (2022), 7581–7596.

[41]

Ramprasaath R. Selvaraju, Michael Cogswell, Abhishek Das, Ramakrishna Vedantam, Devi Parikh, and Dhruv Batra. 2017. Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization. In Proceedings of 2017 IEEE International Conference on Computer Vision (ICCV). 618–626.

[42]

Arushi Sharma, Zefu Hu, Christopher Quinn, and Ali Jannesari. 2023. Interpreting Pretrained Source-code Models using Neuron Redundancy Analyses. arXiv preprint arXiv:2305.00875.

[43]

Avanti Shrikumar, Peyton Greenside, and Anshul Kundaje. 2017. Learning Important Features through Propagating Activation Differences. In Proceedings of the 34th International Conference on Machine Learning - Volume 70 (ICML’17). JMLR.org, 3145–3153.

[44]

David Silver, Julian Schrittwieser, Karen Simonyan, Ioannis Antonoglou, Aja Huang, Arthur Guez, Thomas Hubert, Lucas Baker, Matthew Lai, Adrian Bolton, Yutian Chen, Timothy P. Lillicrap, Fan Hui, Laurent Sifre, George van den Driessche, Thore Graepel, and Demis Hassabis. 2017. Mastering the game of Go without human knowledge. Nat., 550, 7676 (2017), 354–359.

[45]

Yulei Sui and Jingling Xue. 2016. SVF: Interprocedural Static Value-Flow Analysis in LLVM. In Proceedings of the 25th International Conference on Compiler Construction (CC 2016). Association for Computing Machinery, New York, NY, USA. 265–266. isbn:9781450342414

Digital Library

[46]

Sahil Suneja, Yunhui Zheng, Yufan Zhuang, Jim A. Laredo, and Alessandro Morari. 2021. Probing Model Signal-Awareness via Prediction-Preserving Input Minimization. In Proceedings of the 29th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE 2021). Association for Computing Machinery, New York, NY, USA. 945–955. isbn:9781450385626

Digital Library

[47]

Juntao Tan, Shijie Geng, Zuohui Fu, Yingqiang Ge, Shuyuan Xu, Yunqi Li, and Yongfeng Zhang. 2022. Learning and Evaluating Graph Neural Network Explanations Based on Counterfactual and Factual Reasoning. In Proceedings of the ACM Web Conference 2022 (WWW ’22). Association for Computing Machinery, New York, NY, USA. 1018–1027. isbn:9781450390965

Digital Library

[48]

Juntao Tan, Shuyuan Xu, Yingqiang Ge, Yunqi Li, Xu Chen, and Yongfeng Zhang. 2021. Counterfactual Explainable Recommendation. In Proceedings of the 30th ACM International Conference on Information & Knowledge Management (CIKM ’21). Association for Computing Machinery, New York, NY, USA. 1784–1793. isbn:9781450384469

Digital Library

[49]

John Viega, J.T. Bloch, Yoshi Kohno, and Gary McGraw. 2000. ITS4: A static vulnerability scanner for C and C++ code. In Proceedings of the 16th Annual Computer Security Applications Conference. IEEE Computer Society, 257–267.

[50]

Yao Wan, Jingdong Shu, Yulei Sui, Guandong Xu, Zhou Zhao, Jian Wu, and Philip S. Yu. 2020. Multi-modal attention network learning for semantic source code retrieval. In Proceedings of the 34th IEEE/ACM International Conference on Automated Software Engineering (ASE ’19). IEEE Press, 13–25. isbn:9781728125084

[51]

Yao Wan, Wei Zhao, Hongyu Zhang, Yulei Sui, Guandong Xu, and Hai Jin. 2022. What do they capture? a structural analysis of pre-trained language models for source code. In Proceedings of the 44th International Conference on Software Engineering (ICSE ’22). Association for Computing Machinery, New York, NY, USA. 2377–2388. isbn:9781450392211

Digital Library

[52]

Xiangmeng Wang, Qian Li, Dianer Yu, Qing Li, and Guandong Xu. 2024. Reinforced path reasoning for counterfactual explainable recommendation. IEEE Transactions on Knowledge and Data Engineering.

[53]

Xiangmeng Wang, Qian Li, Dianer Yu, Zhichao Wang, Hongxu Chen, and Guandong Xu. 2022. Mgpolicy: Meta graph enhanced off-policy learning for recommendations. In Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval. 1369–1378.

Digital Library

[54]

Yue Wang, Yao Wan, Chenwei Zhang, Lu Bai, Lixin Cui, and Philip Yu. 2019. Competitive Multi-agent Deep Reinforcement Learning with Counterfactual Thinking. In 2019 IEEE International Conference on Data Mining (ICDM). 1366–1371.

[55]

Geemi P. Wellawatte, Aditi Seshadri, and Andrew D. White. 2022. Model agnostic generation of counterfactual explanations for molecules. Chem. Sci., 13 (2022), 3697–3705.

[56]

Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka. 2019. How Powerful are Graph Neural Networks? In Proceedings of the 7th International Conference on Learning Representations, ICLR 2019, New Orleans, LA, USA, May 6-9, 2019. OpenReview.net.

[57]

Fabian Yamaguchi, Nico Golde, Daniel Arp, and Konrad Rieck. 2014. Modeling and Discovering Vulnerabilities with Code Property Graphs. In Proceedings of 2014 IEEE Symposium on Security and Privacy. 590–604.

Digital Library

[58]

Zhitao Ying, Dylan Bourgeois, Jiaxuan You, Marinka Zitnik, and Jure Leskovec. 2019. GNNExplainer: Generating Explanations for Graph Neural Networks. In Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, NeurIPS 2019, December 8-14, 2019, Vancouver, BC, Canada. 9240–9251.

[59]

Dianer Yu, Qian Li, Xiangmeng Wang, Qing Li, and Guandong Xu. 2023. Counterfactual explainable conversational recommendation. IEEE Transactions on Knowledge and Data Engineering.

[60]

Dianer Yu, Qian Li, Xiangmeng Wang, and Guandong Xu. 2023. Deconfounded recommendation via causal intervention. Neurocomputing, 529 (2023), 128–139.

Digital Library

[61]

Dianer Yu, Qian Li, Hongzhi Yin, and Guandong Xu. 2023. Causality-guided graph learning for session-based recommendation. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. 3083–3093.

[62]

Hao Yuan, Haiyang Yu, Shurui Gui, and Shuiwang Ji. 2023. Explainability in Graph Neural Networks: A Taxonomic Survey. IEEE Transactions on Pattern Analysis and Machine Intelligence, 45, 5 (2023), 5782–5799.

Digital Library

[63]

Hao Yuan, Haiyang Yu, Jie Wang, Kang Li, and Shuiwang Ji. 2021. On Explainability of Graph Neural Networks via Subgraph Explorations. In Proceedings of the 38th International Conference on Machine Learning, ICML 2021, 18-24 July 2021, Virtual Event (Proceedings of Machine Learning Research, Vol. 139). PMLR, 12241–12252.

[64]

Andreas Zeller. 2002. Isolating cause-effect chains from computer programs. In Proceedings of the 10th ACM SIGSOFT Symposium on Foundations of Software Engineering (SIGSOFT ’02/FSE-10). Association for Computing Machinery, New York, NY, USA. 1–10. isbn:1581135149

Digital Library

[65]

A. Zeller and R. Hildebrandt. 2002. Simplifying and isolating failure-inducing input. IEEE Transactions on Software Engineering, 28, 2 (2002), 183–200.

Digital Library

[66]

Yaqin Zhou, Shangqing Liu, Jingkai Siow, Xiaoning Du, and Yang Liu. 2019. Devign: Effective Vulnerability Identification by Learning Comprehensive Program Semantics via Graph Neural Networks. In Proceedings of the Advances in Neural Information Processing Systems. 32, Curran Associates, Inc.

[67]

Deqing Zou, Yutao Hu, Wenke Li, Yueming Wu, Haojun Zhao, and Hai Jin. 2022. mVulPreter: A Multi-Granularity Vulnerability Detection System With Interpretations. IEEE Transactions on Dependable and Secure Computing, 1–12.

[68]

Deqing Zou, Yawei Zhu, Shouhuai Xu, Zhen Li, Hai Jin, and Hengkai Ye. 2021. Interpreting Deep Learning-Based Vulnerability Detector Predictions Based on Heuristic Searching. ACM Trans. Softw. Eng. Methodol., 30, 2 (2021), Article 23, mar, 31 pages. issn:1049-331X

Digital Library

Cited By

Brauckmann AFaustino da Silva ASynnaeve GO’Boyle MCastrillon JLeather HKluss DAchour SPalsberg J(2025)DFA-Net: A Compiler-Specific Neural Architecture for Robust Generalization in Data Flow AnalysesProceedings of the 34th ACM SIGPLAN International Conference on Compiler Construction10.1145/3708493.3712687(92-103)Online publication date: 25-Feb-2025
https://dl.acm.org/doi/10.1145/3708493.3712687
Zeng QXiong DWu ZQian KWang YSu Y(2024)TACSan: Enhancing Vulnerability Detection with Graph Neural NetworkElectronics10.3390/electronics1319381313:19(3813)Online publication date: 26-Sep-2024
https://doi.org/10.3390/electronics13193813
Chang WYe CZhou H(2024)Fine-Tuning Pre-trained Model with Optimizable Prompt Learning for Code Vulnerability Detection2024 IEEE 35th International Symposium on Software Reliability Engineering (ISSRE)10.1109/ISSRE62328.2024.00021(108-119)Online publication date: 28-Oct-2024
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Index Terms

Graph Neural Networks for Vulnerability Detection: A Counterfactual Explanation
1. Software and its engineering
  1. Software organization and properties
    1. Extra-functional properties
      1. Software reliability

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cover image ACM Conferences

ISSTA 2024: Proceedings of the 33rd ACM SIGSOFT International Symposium on Software Testing and Analysis

September 2024

1928 pages

ISBN:9798400706127

DOI:10.1145/3650212

General Chair:
Maria Christakis
TU Wien, Austria
,
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Michael Pradel
University of Stuttgart, Germany

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ISSTA '24: 33rd ACM SIGSOFT International Symposium on Software Testing and Analysis

September 16 - 20, 2024

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Cited By

Brauckmann AFaustino da Silva ASynnaeve GO’Boyle MCastrillon JLeather HKluss DAchour SPalsberg J(2025)DFA-Net: A Compiler-Specific Neural Architecture for Robust Generalization in Data Flow AnalysesProceedings of the 34th ACM SIGPLAN International Conference on Compiler Construction10.1145/3708493.3712687(92-103)Online publication date: 25-Feb-2025
https://dl.acm.org/doi/10.1145/3708493.3712687
Zeng QXiong DWu ZQian KWang YSu Y(2024)TACSan: Enhancing Vulnerability Detection with Graph Neural NetworkElectronics10.3390/electronics1319381313:19(3813)Online publication date: 26-Sep-2024
https://doi.org/10.3390/electronics13193813
Chang WYe CZhou H(2024)Fine-Tuning Pre-trained Model with Optimizable Prompt Learning for Code Vulnerability Detection2024 IEEE 35th International Symposium on Software Reliability Engineering (ISSRE)10.1109/ISSRE62328.2024.00021(108-119)Online publication date: 28-Oct-2024
https://doi.org/10.1109/ISSRE62328.2024.00021
Le TKim JLee SKim H(2024)Robust Vulnerability Detection in Solidity-Based Ethereum Smart Contracts Using Fine-Tuned Transformer Encoder ModelsIEEE Access10.1109/ACCESS.2024.348238912(154700-154717)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3482389

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