The DOI matches its Crossref record; 10 of the 10 references it cites were found in a registry or an index.
Vulnerability detection with fine-grained interpretations
Open accessAbstract
Despite the successes of machine learning (ML) and deep learning (DL)-based vulnerability detectors (VD), they are limited to providing only the decision on whether a given code is vulnerable or not, without details on what part of the code is relevant to the detected vulnerability. We present IVDetect, an interpretable vulnerability detector with the philosophy of using Artificial Intelligence (AI) to detect vulnerabilities, while using Intelligence Assistant (IA) to provide VD interpretations in terms of vulnerable statements.
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Cites
Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling
Chung, J., Gulcehre, C., Cho, K., & Bengio, Y. (2014) · arXiv · doi:10.48550/arxiv.1412.3555 · Cited by 11,008
Automated Vulnerability Detection in Source Code Using Deep Representation Learning
Russell, R., Kim, L., Hamilton, L., Lazovich, T., Harer, J., Ozdemir, O., Ellingwood, P., & McConley, M. (2018) · 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA) · doi:10.1109/icmla.2018.00120 · Cited by 623
A C/C++ Code Vulnerability Dataset with Code Changes and CVE Summaries
Fan, J., Li, Y., Wang, S., & Nguyen, T. N. (2020) · Proceedings of the 17th International Conference on Mining Software Repositories · doi:10.1145/3379597.3387501 · Cited by 498
Self-citationPredicting vulnerable software components
Neuhaus, S., Zimmermann, T., Holler, C., & Zeller, A. (2007) · Proceedings of the 14th ACM conference on Computer and communications security · doi:10.1145/1315245.1315311 · Cited by 416
Predicting Vulnerable Software Components via Text Mining
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ITS4: a static vulnerability scanner for C and C++ code
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Graph-based mining of multiple object usage patterns
Nguyen, T. T., Nguyen, H. A., Pham, N. H., Al-Kofahi, J. M., & Nguyen, T. N. (2009) · Proceedings of the 7th joint meeting of the European software engineering conference and the ACM SIGSOFT symposium on The foundations of software engineering · doi:10.1145/1595696.1595767 · Cited by 292
Self-citationGeneralized vulnerability extrapolation using abstract syntax trees
Yamaguchi, F., Lottmann, M., & Rieck, K. (2012) · Proceedings of the 28th Annual Computer Security Applications Conference · doi:10.1145/2420950.2421003 · Cited by 229
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Self-citationKeller, F., Grunske, L., Heiden, S., Filieri, A., van Hoorn, A., & Lo, D. (2017) · IEEE International Conference on Software Quality, Reliability and Security (QRS) · doi:10.1109/qrs.2017.22 · Cited by 83
Cited by
LineVul: a transformer-based line-level vulnerability prediction
Fu, M., & Tantithamthavorn, C. (2022) · Proceedings of the 19th International Conference on Mining Software Repositories · doi:10.1145/3524842.3528452 · Cited by 384
A survey on large language models for software engineering
Zhang, Q., Fang, C., Xie, Y., Zhang, Y., Yu, S., Sun, W., Yang, Y., & Chen, Z. (2026) · Science China Information Sciences · doi:10.1007/s11432-025-4670-0 · Cited by 124
Understanding the Effectiveness of Large Language Models in Detecting Security Vulnerabilities
Khare, A., Dutta, S., Li, Z., Solko-Breslin, A., Alur, R., & Naik, M. (2023) · arXiv · doi:10.48550/arxiv.2311.16169 · Cited by 64
Natural Is the Best: Model-Agnostic Code Simplification for Pre-trained Large Language Models
Wang, Y., Li, X., Nguyen, T. N., Wang, S., Ni, C., & Ding, L. (2024) · Proceedings of the ACM on Software Engineering · doi:10.1145/3643753 · Cited by 6
Self-citationSimClone: Detecting Tabular Data Clones Using Value Similarity
Yang, X., Rajbahadur, G. K., Lin, D., Wang, S., & Jiang, Z. M. (. (2025) · ACM Transactions on Software Engineering and Methodology · doi:10.1145/3676961 · Cited by 3
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Metadata
- Authors
- Li, Y., Wang, S., & Nguyen, T. N.
- Venue
- Proceedings of the 29th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering
- Publisher
- ACM
- Published
- 2021-08-18
- Pages
- 292-303
- OpenAlex
- W3177116043
- Cited by
- 282
- References
- 10
- FWCI
- 27.30
- Type
- conference paper
- Language
- en
- Open access
- green
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- Li, Y., Wang, S., & Nguyen, T. N. (2021). Vulnerability detection with fine-grained interpretations. Proceedings of the 29th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering. https://doi.org/10.1145/3468264.3468597
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