Zobrazeno 1 - 8
of 8
pro vyhledávání: '"Gruner, Bernd"'
Type inference methods based on deep learning are becoming increasingly popular as they aim to compensate for the drawbacks of static and dynamic analysis approaches, such as high uncertainty. However, their practical application is still debatable d
Externí odkaz:
http://arxiv.org/abs/2308.02675
Autor:
Sonnekalb, Tim, Knaust, Christopher-Tobias, Gruner, Bernd, Brust, Clemens-Alexander, von Kurnatowski, Lynn, Schreiber, Andreas, Heinze, Thomas S., Mäder, Patrick
Static analysis tools come in many forms andconfigurations, allowing them to handle various tasks in a (secure) development process: code style linting, bug/vulnerability detection, verification, etc., and adapt to the specific requirements of a soft
Externí odkaz:
http://arxiv.org/abs/2304.01725
Transformer networks such as CodeBERT already achieve outstanding results for code clone detection in benchmark datasets, so one could assume that this task has already been solved. However, code clone detection is not a trivial task. Semantic code c
Externí odkaz:
http://arxiv.org/abs/2208.12588
Publikováno v:
2023 IEEE/ACM 20th International Conference on Mining Software Repositories (MSR), Melbourne, Australia, 2023, pp. 158-169
Optional type annotations allow for enriching dynamic programming languages with static typing features like better Integrated Development Environment (IDE) support, more precise program analysis, and early detection and prevention of type-related ru
Externí odkaz:
http://arxiv.org/abs/2208.09189
Automatic vulnerability detection on C/C++ source code has benefitted from the introduction of machine learning to the field, with many recent publications targeting this combination. In contrast, assembly language or machine code artifacts receive l
Externí odkaz:
http://arxiv.org/abs/2112.06623
Deep-learning methods offer unsurpassed recognition performance in a wide range of domains, including fine-grained recognition tasks. However, in most problem areas there are insufficient annotated training samples. Therefore, the topic of transfer l
Externí odkaz:
http://arxiv.org/abs/2110.11778
Publikováno v:
In Computers & Security May 2023 128
