21 citations · 37 across the 13 of their papers we have counts for
13 papers
TIPICAL -- Type Inference for Python In Critical Accuracy Level
Jonathan Elkobi, Bernd Gruner, Tim Sonnekalb +1
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…
A Static Analysis Platform for Investigating Security Trends in Repositories
Tim Sonnekalb, Christopher-Tobias Knaust, Bernd Gruner +5
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…
Generalizability of Code Clone Detection on CodeBERT
Tim Sonnekalb, Bernd Gruner, Clemens-Alexander Brust +1
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…
Cross-Domain Evaluation of a Deep Learning-Based Type Inference System
Bernd Gruner, Tim Sonnekalb, Thomas S. Heinze +1
Optional type annotations allow for enriching dynamic programming languages with static typing features like better Integrated Development Environment (IDE) support, more precise p…
ROMEO: Exploring Juliet through the Lens of Assembly Language
Clemens-Alexander Brust, Tim Sonnekalb, Bernd Gruner
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 combinati…
Self-Supervised Learning from Semantically Imprecise Data
Clemens-Alexander Brust, Björn Barz, Joachim Denzler
Learning from imprecise labels such as "animal" or "bird", but making precise predictions like "snow bunting" at inference time is an important capability for any classifier when e…