Johannes Villmow

According to our database, Johannes Villmow authored at least 8 papers between 2018 and 2024.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2024

Evaluating Contextualized Code Search in Practical User Studies.

Johannes Villmow, Adrian Ulges, Ulrich Schwanecke

Proceedings of the 54. Jahrestagung der Gesellschaft für Informatik, 2024

2023

How Well Can Masked Language Models Spot Identifiers That Violate Naming Guidelines?

Johannes Villmow, Viola Campos, Jean Petry, Amine Abbad Andaloussi, Adrian Ulges, Barbara Weber

Proceedings of the 23rd IEEE International Working Conference on Source Code Analysis and Manipulation, 2023

Value Stream Repair Using Graph Structure Learning.

Marco Wrzalik, Julian Eversheim, Johannes Villmow, Adrian Ulges, Dirk Krechel, Sven Spieckermann, Robert Forstner

Proceedings of the Advances and Trends in Artificial Intelligence. Theory and Applications, 2023

2022

Addressing Leakage in Self-Supervised Contextualized Code Retrieval.

Johannes Villmow, Viola Campos, Adrian Ulges, Ulrich Schwanecke

Proceedings of the 29th International Conference on Computational Linguistics, 2022

2021

A Structural Transformer with Relative Positions in Trees for Code-to-Sequence Tasks.

Johannes Villmow, Adrian Ulges, Ulrich Schwanecke

Proceedings of the International Joint Conference on Neural Networks, 2021

2020

Bidirectional Transformer Language Models for Smart Autocompletion of Source Code.

Felix Binder, Johannes Villmow, Adrian Ulges

Proceedings of the 50. Jahrestagung der Gesellschaft für Informatik, INFORMATIK 2020 - Back to the Future, Karlsruhe, Germany, 28. September, 2020

2019

An Open-World Extension to Knowledge Graph Completion Models.

Haseeb Shah, Johannes Villmow, Adrian Ulges, Ulrich Schwanecke, Faisal Shafait

Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence, 2019

2018

Automatic Keyphrase Extraction Using Recurrent Neural Networks.

Johannes Villmow, Marco Wrzalik, Dirk Krechel

Proceedings of the Machine Learning and Data Mining in Pattern Recognition, 2018