Zobrazeno 1 - 10
of 340
pro vyhledávání: '"Steenhoek, A."'
Autor:
Steenhoek, Benjamin, Rahman, Md Mahbubur, Roy, Monoshi Kumar, Alam, Mirza Sanjida, Barr, Earl T., Le, Wei
Large Language Models (LLMs) have demonstrated great potential for code generation and other software engineering tasks. Vulnerability detection is of crucial importance to maintaining the security, integrity, and trustworthiness of software systems.
Externí odkaz:
http://arxiv.org/abs/2403.17218
Recently, pretrained language models have shown state-of-the-art performance on the vulnerability detection task. These models are pretrained on a large corpus of source code, then fine-tuned on a smaller supervised vulnerability dataset. Due to the
Externí odkaz:
http://arxiv.org/abs/2311.04109
Software testing is a crucial aspect of software development, and the creation of high-quality tests that adhere to best practices is essential for effective maintenance. Recently, Large Language Models (LLMs) have gained popularity for code generati
Externí odkaz:
http://arxiv.org/abs/2310.02368
Software often fails in the field, however reproducing and debugging field failures is very challenging: the failure-inducing input may be missing, and the program setup can be complicated and hard to reproduce by the developers. In this paper, we pr
Externí odkaz:
http://arxiv.org/abs/2309.11004
Most existing pre-trained language models for source code focus on learning the static code text, typically augmented with static code structures (abstract syntax tree, dependency graphs, etc.). However, program semantics will not be fully exposed be
Externí odkaz:
http://arxiv.org/abs/2306.07487
Static analysis is widely used for software assurance. However, static analysis tools can report an overwhelming number of warnings, many of which are false positives. Applying static analysis to a new version, a large number of warnings can be only
Externí odkaz:
http://arxiv.org/abs/2305.02515
Deep learning (DL) models of code have recently reported great progress for vulnerability detection. In some cases, DL-based models have outperformed static analysis tools. Although many great models have been proposed, we do not yet have a good unde
Externí odkaz:
http://arxiv.org/abs/2212.08109
Deep learning-based vulnerability detection has shown great performance and, in some studies, outperformed static analysis tools. However, the highest-performing approaches use token-based transformer models, which are not the most efficient to captu
Externí odkaz:
http://arxiv.org/abs/2212.08108
Static analysis is an important approach for finding bugs and vulnerabilities in software. However, inspecting and confirming static warnings are challenging and time-consuming. In this paper, we present a novel solution that automatically generates
Externí odkaz:
http://arxiv.org/abs/2106.04735
Autor:
Havas, Aaron P., Tula-Sanchez, Ana A., Steenhoek, Hailey M., Bhakta, Anvi, Wingfield, Taylor, Huntley, Matthew J., Nofal, Angela S., Ahmed, Tasmia, Jaime-Frias, Rosa, Smith, Catharine L.
Publikováno v:
In Translational Oncology January 2024 39
