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View article: Accurate Architectural Threat Elicitation From Source Code Through Hybrid Information Flow Analysis
Accurate Architectural Threat Elicitation From Source Code Through Hybrid Information Flow Analysis Open
Software processes a vast amount of sensitive data. However, tracing information flows in complex programs and eliciting threats, which, for example, could lead to information leaks, pose significant challenges. The problem lies in the abs…
View article: TIPICAL -- Type Inference for Python In Critical Accuracy Level
TIPICAL -- Type Inference for Python In Critical Accuracy Level Open
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 …
View article: A Static Analysis Platform for Investigating Security Trends in Repositories
A Static Analysis Platform for Investigating Security Trends in Repositories Open
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 requirement…
View article: Generalizability of Code Clone Detection on CodeBERT
Generalizability of Code Clone Detection on CodeBERT Open
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. Sema…
View article: Generalizability of Code Clone Detection on CodeBERT
Generalizability of Code Clone Detection on CodeBERT Open
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. Sema…
View article: Cross-Domain Evaluation of a Deep Learning-Based Type Inference System
Cross-Domain Evaluation of a Deep Learning-Based Type Inference System Open
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…
View article: CrossDomainTypes4Py: A Python Dataset for Cross-Domain Evaluation of Type Inference Systems
CrossDomainTypes4Py: A Python Dataset for Cross-Domain Evaluation of Type Inference Systems Open
This dataset contains python repositories mined on GitHub on January 20, 2021. It allows a cross-domain evaluation of type inference systems. For this purpose, it consists of two sub-datasets, each containing only projects from the web or …
View article: CrossDomainTypes4Py: A Python Dataset for Cross-Domain Evaluation of Type Inference Systems
CrossDomainTypes4Py: A Python Dataset for Cross-Domain Evaluation of Type Inference Systems Open
This dataset contains python repositories mined on GitHub on January 20, 2021. It allows a cross-domain evaluation of type inference systems. For this purpose, it consists of two sub-datasets, each containing only projects from the web or …
View article: ROMEO: Exploring Juliet through the Lens of Assembly Language
ROMEO: Exploring Juliet through the Lens of Assembly Language Open
Automatic vulnerability detection on C/C++ source code has benefitted from the introduction of machine learning to the field, with many recent publications considering this combination. In contrast, assembly language or machine code artifa…
View article: ROMEO: Exploring Juliet through the Lens of Assembly Language
ROMEO: Exploring Juliet through the Lens of Assembly Language Open
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 artifact…
View article: Domain Adaptation and Active Learning for Fine-Grained Recognition in the Field of Biodiversity
Domain Adaptation and Active Learning for Fine-Grained Recognition in the Field of Biodiversity Open
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…