An empirical study of deep learning models for vulnerability detection B Steenhoek, MM Rahman, R Jiles, W Le 2023 IEEE/ACM 45th International Conference on Software Engineering (ICSE …, 2023 | 54* | 2023 |
Traced: Execution-aware pre-training for source code Y Ding, B Steenhoek, K Pei, G Kaiser, W Le, B Ray Proceedings of the 46th IEEE/ACM International Conference on Software …, 2024 | 19 | 2024 |
Dataflow analysis-inspired deep learning for efficient vulnerability detection B Steenhoek, H Gao, W Le Proceedings of the 46th IEEE/ACM International Conference on Software …, 2024 | 12* | 2024 |
Reinforcement learning from automatic feedback for high-quality unit test generation B Steenhoek, M Tufano, N Sundaresan, A Svyatkovskiy arXiv preprint arXiv:2310.02368, 2023 | 12 | 2023 |
Validating static warnings via testing code fragments A Kallingal Joshy, X Chen, B Steenhoek, W Le Proceedings of the 30th ACM SIGSOFT International Symposium on Software …, 2021 | 11 | 2021 |
A Comprehensive Study of the Capabilities of Large Language Models for Vulnerability Detection B Steenhoek, MM Rahman, MK Roy, MS Alam, ET Barr, W Le arXiv preprint arXiv:2403.17218, 2024 | 8 | 2024 |
Do Language Models Learn Semantics of Code? A Case Study in Vulnerability Detection B Steenhoek, MM Rahman, S Sharmin, W Le arXiv preprint arXiv:2311.04109, 2023 | 1 | 2023 |
Reproducing Failures in Fault Signatures AK Joshy, B Steenhoek, X Guo, W Le arXiv preprint arXiv:2309.11004, 2023 | | 2023 |
A Study of Static Warning Cascading Tools (Experience Paper) X Guo, AK Joshy, B Steenhoek, W Le, L Flynn arXiv preprint arXiv:2305.02515, 2023 | | 2023 |
Refactoring programs to improve the performance of deep learning for vulnerability detection BJ Steenhoek Iowa State University, 2021 | | 2021 |