Dominik Fuchß

Researcher at Karlsruhe Institute of Technology (KIT), Germany.

My overall goal is the utilization of informal artifacts for automatic traceability link recovery (TLR) and consistency checking. In my recent research I focus on the application of large language models for TLR tasks, such as retrieval augmented generation.

If you want to contact me securely, please use my PGP key.

My research area and interests summarized in a short listing:

Natural Language Processing
Traceability Link Recovery
Consistency between formal and informal artifacts
Retrieval Augmented Generation & Large Language Models for Software Engineering

During my research and teaching I’m involved in several development projects. A small excerpt is shown below.

Member of the Main Development Team of ArDoCo
Administrator of our Artemis infrastructure that is used for the Programming Lecture, Software Engineering I, and more lectures
Product Owner of multiple projects of kit-sdq
Member of the Maintainer Team of JPlag

latest posts

Oct 31, 2024	al-folio-conf
Aug 30, 2024	ArDoCo Metrics Calculator
Aug 09, 2024	YARB @ TWIM

selected publications

ECSA

Establishing a Benchmark Dataset for Traceability Link Recovery Between Software Architecture Documentation and Models

Dominik Fuchß, Sophie Corallo, Jan Keim, Janek Speit, and 1 more author

In Software Architecture. ECSA 2022 Tracks and Workshops, 2023

Bib DOI PRE

@inproceedings{fuchss_establishing_2023,
  author = {Fuch{\ss}, Dominik and Corallo, Sophie and Keim, Jan and Speit, Janek and Koziolek, Anne},
  editor = {Batista, Thais and Bure{\v{s}}, Tom{\'a}{\v{s}} and Raibulet, Claudia and Muccini, Henry},
  title = {Establishing a Benchmark Dataset for Traceability Link Recovery Between Software Architecture Documentation and Models},
  booktitle = {Software Architecture. ECSA 2022 Tracks and Workshops},
  year = {2023},
  publisher = {Springer International Publishing},
  address = {Cham},
  pages = {455--464},
  doi = {10.1007/978-3-031-36889-9_30},
}

A Formalized Classification Schema for Model Consistency

Thomas Kühn, Dominik Fuchß, Sophie Corallo, Lars König, and 7 more authors

2023

Bib DOI PDF

@techreport{kuehn_formalized_2023,
  author = {Kühn, Thomas and Fuchß, Dominik and Corallo, Sophie and König, Lars and Burger, Erik and Keim, Jan and Mazkatli, Manar and Sağlam, Timur and Reiche, Frederik and Koziolek, Anne and Reussner, Ralf},
  year = {2023},
  title = {A Formalized Classification Schema for Model Consistency},
  doi = {10.5445/IR/1000161127},
  institution = {{Karlsruher Institut für Technologie (KIT)}},
  issn = {2190-4782},
  series = {Karlsruhe Reports in Informatics},
  pagetotal = {37},
  language = {english},
  volume = {2023},
  number = {1},
}

ICSE
Recovering Trace Links Between Software Documentation And Code

Jan Keim, Sophie Corallo, Dominik Fuchß, Tobias Hey, and 2 more authors

In Proceedings of the IEEE/ACM 46th International Conference on Software Engineering, Lisbon, Portugal, 2024

Bib DOI PRE

Introduction Software development involves creating various artifacts at different levels of abstraction and establishing relationships between them is essential. Traceability link recovery (TLR) automates this process, enhancing software quality by aiding tasks like maintenance and evolution. However, automating TLR is challenging due to semantic gaps resulting from different levels of abstraction. While automated TLR approaches exist for requirements and code, architecture documentation lacks tailored solutions, hindering the preservation of architecture knowledge and design decisions. Methods This paper presents our approach TransArC for TLR between architecture documentation and code, using component-based architecture models as intermediate artifacts to bridge the semantic gap. We create transitive trace links by combining the existing approach ArDoCo for linking architecture documentation to models with our novel approach ArCoTL for linking architecture models to code.Results We evaluate our approaches with five open-source projects, comparing our results to baseline approaches. The model-to-code TLR approach achieves an average F1-score of 0.98, while the documentation-to-code TLR approach achieves a promising average F1-score of 0.82, significantly outperforming baselines. Conclusion Combining two specialized approaches with an intermediate artifact shows promise for bridging the semantic gap. In future research, we will explore further possibilities for such transitive approaches.
@inproceedings{keim_recovering_2024, author = {Keim, Jan and Corallo, Sophie and Fuch\ss{}, Dominik and Hey, Tobias and Telge, Tobias and Koziolek, Anne}, title = {Recovering Trace Links Between Software Documentation And Code}, year = {2024}, isbn = {9798400702174}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, url = {https://doi.org/10.1145/3597503.3639130}, doi = {10.1145/3597503.3639130}, booktitle = {Proceedings of the IEEE/ACM 46th International Conference on Software Engineering}, articleno = {215}, numpages = {13}, location = {Lisbon, Portugal}, series = {ICSE '24}, }