scholarly journals Technical Debt indication in PLC Code for automated Production Systems: Introducing a Domain Specific Static Code Analysis Tool

2018 ◽  
Vol 51 (10) ◽  
pp. 70-75 ◽  
Author(s):  
Safa Bougouffa ◽  
Quang Huan Dong ◽  
Sebastian Diehm ◽  
Fabian Gemein ◽  
Birgit Vogel-Heuser
Keyword(s):  
Production Systems ◽  
Analysis Tool ◽  
Technical Debt ◽  
Code Analysis ◽  
Automated Production ◽  
Domain Specific ◽  
Static Code Analysis

scholarly journals Technical Debt as indicator for weaknesses in engineering of automated production systems

2019 ◽  
Vol 13 (3-4) ◽  
pp. 273-282
Author(s):  
Quang Huan Dong ◽  
Felix Ocker ◽  
Birgit Vogel-Heuser
Keyword(s):  
Production Systems ◽  
Technical Debt ◽  
Automated Production

scholarly journals sharpniZer: A C# Static Code Analysis Tool for Mission Critical Systems

2020 ◽  
Vol 5 (6) ◽  
pp. 561-570
Author(s):  
Arooba Shahoor ◽  
Rida Shaukat ◽  
Sumaira Sultan Minhas ◽  
Hina Awan ◽  
Kashif Saghar
Keyword(s):  
Analysis Tool ◽  
Critical Systems ◽  
Code Analysis ◽  
Static Code Analysis ◽  
Mission Critical

scholarly journals Model-Driven Approach for Realization of Data Collection Architectures for Cyber-Physical Systems of Systems to Lower Manual Implementation Efforts

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 745
Author(s):  
Emanuel Trunzer ◽  
Birgit Vogel-Heuser ◽  
Jan-Kristof Chen ◽  
Moritz Kohnle
Keyword(s):  
Data Collection ◽  
Production Systems ◽  
Information Gain ◽  
Model Transformations ◽  
Systems Of Systems ◽  
Automated Production ◽  
Model Driven ◽  
Domain Specific ◽  
Wide Scale ◽  
Model Driven Approach

Data collection from distributed automated production systems is one of the main prerequisites to leverage information gain from data analysis in the context of Industrie 4.0, e.g., for the optimization of product quality. However, the realization of data collection architectures is associated with immense implementation efforts due to the heterogeneity of systems, protocols, and interfaces, as well as the multitude of involved disciplines in such projects. Therefore, this paper contributes with an approach for the model-driven generation of data collection architectures to significantly lower manual implementation efforts. Via model transformations, the corresponding source code is automatically generated from formalized models that can be created using a graphical domain-specific language. The automatically generated architecture features support for various established IIoT protocols. In a lab-scale evaluation and a unique generalized extrapolation study, the significant effort savings compared to manual programming could be quantified. In conclusion, the proposed approach can successfully mitigate the current scientific and industrial challenges to enable wide-scale access to industrial data.

Sign in / Sign up

Export Citation Format

Share Document