XITASO invests in research and innovation in order to be and to remain an excellent digitalisation partner. Our method is to examine future topics intensively with partners from the worlds of science and business and work out possible applications for our partners and ourselves. This is how we provide space for the best possible application-oriented research and a link to today’s and future markets.
Scientific excellence as well as national and international exchanges with the scientific community are as much part of research at XITASO as self organised work in an agile, company-wide research team. We are convinced: this is how today’s research creates tomorrow’s innovations.
The research project “Test the Test” (T3) has set itself the objectives of automatically evaluating the effectiveness of model, software and hardware tests and thereby improving the quality of said tests. With the aid of Fault Injection and Mutations, hardware interfaces, code and system models are deliberately and systematically seeded with errors, in order to evaluate corresponding tests regarding their quality and effectiveness in finding each and every error. The improvement of testing quality helps satisfy the ever-increasing quality demands of embedded systems.
The objective of the project “Automated Commissioning through Persistence of Expert Knowledge” (ACPEK) is on the one hand the automation of fine adjustment of machine parameters through machine learning techniques. On the other hand, expert knowledge should be persisted mechanically in order to maintain it despite the demographic changes. The combination of both aspects makes it possible to simplify and accelerate the mechanical learning process with the aid of expert knowledge. The approach is evaluated on 3d printing that can be seen as a representative of manufacturing processes that are dependent on ambient conditions.
Das Forschungsvorhaben “Test the Test” (T3) hat sich zum Ziel gesetzt, die Effektivität von Model-, Software- und Hardwaretests automatisiert zu bewerten und damit die Qualität der Tests zu verbessern. Mithilfe von Fault Injection und Mutationen werden Hardwareschnittstellen, Code und Systemmodelle absichtlich und systematisch mit Fehlern behaftet, um entsprechende Tests hinsichtlich ihrer Qualität und Effektivität beim Auffinden eben jener Fehler bewerten zu können. Die Verbesserung der Testqualität hilft, den immer größer werdenden Qualitätsanforderungen eingebetteter Systeme zu genügen.
Ziel des Projekts „Automatisierte Inbetriebnahme durch Persistierung von Expertenwissen“ (AIPE) ist es zum einen, die Feineinstellung von Maschinenparametern durch maschinelle Lernverfahren zu automatisieren. Zum anderen soll Expertenwissen maschinell persistiert werden, um es trotz des demographischen Wandels erhalten zu können. Die Zusammenführung beider Bereiche ermöglicht es, die maschinellen Lernprozesse mithilfe des Expertenwissens zu vereinfachen und zu beschleunigen. Im Projekt wird 3D-Druck als Stellvertreter für Fertigungsprozesse untersucht, die von Umgebungseinflüssen abhängen.
Ziel des Projekts HITSSSE ist die Verbesserung der IT-Sicherheit durch sichere Software-Entwicklung für kleine und mittlere Unternehmen. Generische Lösungsansätze sollen hier KMUs in Deutschland helfen, IT-Sicherheit kosteneffizient und einfach nutzbar zu machen. XITASO bringt seine Expertise als assoziierter Partner in dem Projekt ein.
Measuring Similarities in Model Structure of Metaheuristic Rule Set Learners
David Pätzel, Richard Nordsieck, Jörg Hähner
EvoAPPS 2024
Tracking assets in source code with Security Annotations
Daniel Haak, Raphael Mayr, Jan-Philipp Steghöfer, Alexandra Teynor, Phillip Heidegger
ICSE 2024 Poster Track
Where Requirements and Agility Meet: No Man’s Land or a Land of Opportunity?
Fabiano Dalpiaz, Jan-Philipp Steghöfer
To appear in IEEE Software
Combining Requirements Enigneering Techniques for the Analysis of a Legacy System
Jessica Friedline, Jan-Philipp Steghöfer
Joint Proceedings of REFSQ-2024 Workshops, Doctoral Symposium, Posters & Tools Track and Education and Training Track. Co-located with REFSQ 2024. Winterthur, Switzerland, April 8, 2024
FeatRacer: Locating Features Through Assisted Traceability
Mukelabai Mukelabai, Kevin Hermann, Thorsten Berger, Jan-Philipp Steghöfer
IEEE Transactions on Software Engineering, 2023
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Processes, Methods, and Tools in Model-based Engineering — A Qualitative Multiple-Case Study
Jörg Holtmann, Grischa Liebel, Jan-Philipp Steghöfer
Journal of Software and Systems, 2023
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Blended modeling in commercial and open-source model-driven software engineering tools: A systematic study.
Istvan David, Malvina Latifaj, Jakob Pietron, Weixing Zhang, Federico Ciccozzi, Ivano Malavolta, Alexander Raschke, Jan-Philipp Steghöfer, Regina Hebig
Softw. Syst. Model.22(1): 415-447 (2023)
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CASCADE: An Asset-driven Approach to Build Security Assurance Cases for Automotive Systems.
Mazen Mohamad, Rodi Jolak, Örjan Askerdal, Jan-Philipp Steghöfer, Riccardo Scandariato
ACM Trans. Cyber Phys. Syst. 7(1): 3:1-3:26 (2023)
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Trustful Model-Based Information Exchange in Collaborative Engineering.
David Schmelter, Jan-Philipp Steghöfer, Karsten Albers, Mats Ekman, Jörg Tessmer, Raphael Weber
EuroSPI (1) 2023: 156-170
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Exploiting Meta-Model Structures in the Generation of Xtext Editors.
Jörg Holtmann, Jan-Philipp Steghöfer, Weixing Zhang.
Proceedings of the 11th International Conference on Model-Based Software and Systems Engineering (MODELSWARD).
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Creating Python-style Domain Specific Languages: A Semi-automated Approach and Intermediate Results.
Weixing Zhang, Regina Hebig, Jan-Philipp Steghöfer, Jörg Holtmann.
Proceedings of the 11th International Conference on Model-Based Software and Systems Engineering (MODELSWARD).
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Automated Extraction of Grammar Optimization Rule Configurations for Metamodel-Grammar Co-evolution.
Weixing Zhang, Regina Hebig, Daniel Strüber, Jan-Philipp Steghöfer
SLE 2023: 84-96
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Dr. Andreas Angerer
Head of Research and Innovation
XITASO GmbH
Dr. Andreas Angerer
Phone +49 821 885882-94
andreas.angerer@xitaso.com