The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
LineVul: a transformer-based line-level vulnerability prediction
Abstract
Software vulnerabilities are prevalent in software systems, causing a variety of problems including deadlock, information loss, or system failures. Thus, early predictions of software vulnerabilities are critically important in safety-critical software systems. Various ML/DL-based approaches have been proposed to predict vulnerabilities at the file/function/method level. Recently, IVDetect (a graph-based neural network) is proposed to predict vulnerabilities at the function level. Yet, the IVDetect approach is still inaccurate and coarse-grained. In this paper, we propose LineVul, a Transformer-based line-level vulnerability prediction approach in order to address several limitations of the state-of-the-art IVDetect approach. Through an empirical evaluation of a large-scale real-world dataset with 188k+ C/C++ functions, we show that LineVul achieves (1) 160%-379% higher F1-measure for function-level predictions; (2) 12%-25% higher Top-10 Accuracy for line-level predictions; and (3) 29%-53% less [email protected]%Recall than the baseline approaches, highlighting the significant advancement of LineVul towards more accurate and more cost-effective line-level vulnerability predictions. Our additional analysis also shows that our LineVul is also very accurate (75%-100%) for predicting vulnerable functions affected by the Top-25 most dangerous CWEs, highlighting the potential impact of our LineVul in real-world usage scenarios.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Transformers: State-of-the-Art Natural Language Processing
Wolf, T., Debut, L., Sanh, V., Chaumond, J., Delangue, C., Moi, A., Cistac, P., Rault, T., Louf, R., Funtowicz, M., et al. (2020) · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations · doi:10.18653/v1/2020.emnlp-demos.6 · Cited by 8,741
Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps
Simonyan, K., Vedaldi, A., & Zisserman, A. (2013) · arXiv · doi:10.48550/arxiv.1312.6034 · Cited by 5,370
CodeBERT: A Pre-Trained Model for Programming and Natural Languages
Feng, Z., Guo, D., Tang, D., Duan, N., Feng, X., Gong, M., Shou, L., Qin, B., Liu, T., Jiang, D., et al. (2020) · Findings of the Association for Computational Linguistics: EMNLP 2020 · doi:10.18653/v1/2020.findings-emnlp.139 · Cited by 2,824
Big code != big vocabulary: open-vocabulary models for source code
Karampatsis, R.-M., Babii, H., Robbes, R., Sutton, C., & Janes, A. (2020) · Proceedings of the ACM/IEEE 42nd International Conference on Software Engineering · doi:10.1145/3377811.3380342 · Cited by 182
Predicting Defective Lines Using a Model-Agnostic Technique
Wattanakriengkrai, S., Thongtanunam, P., Tantithamthavorn, C., Hata, H., & Matsumoto, K. (2022) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2020.3023177 · Cited by 137
Self-citation
Cited by
Generative AI in cybersecurity: A comprehensive review of LLM applications and vulnerabilities
Ferrag, M. A., Alwahedi, F., Battah, A., Cherif, B., Mechri, A., Tihanyi, N., Bisztray, T., & Debbah, M. (2025) · Internet of Things and Cyber-Physical Systems · doi:10.1016/j.iotcps.2025.01.001 · Cited by 148
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
Human-In-the-Loop Software Development Agents
Takerngsaksiri, W., Pasuksmit, J., Thongtanunam, P., Tantithamthavorn, C., Zhang, R., Jiang, F., Li, J., Cook, E., Chen, K., & Wu, M. (2024) · arXiv · doi:10.48550/arxiv.2411.12924 · Cited by 24
Self-citation
Metadata
- Authors
- Fu, M., & Tantithamthavorn, C.
- Venue
- Proceedings of the 19th International Conference on Mining Software Repositories
- Publisher
- ACM
- Published
- 2022-05-23
- Pages
- 608-620
- OpenAlex
- W4312436517
- Cited by
- 384
- References
- 5
- FWCI
- 62.61
- Type
- conference paper
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: LineVul: a transformer-based line-level vulnerability prediction. https://app.cochat.ai/papers/10.1145/3524842.3528452
- The paper
- Fu, M., & Tantithamthavorn, C. (2022). LineVul: a transformer-based line-level vulnerability prediction. Proceedings of the 19th International Conference on Mining Software Repositories. https://doi.org/10.1145/3524842.3528452
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
