Metrics for software quality in automated production systems as an indicator for technical debt

2017 ◽  
Author(s):  
Lorena Capitan ◽  
Birgit Vogel-Heuser
Keyword(s):  
Software Quality ◽  
Production Systems ◽  
Technical Debt ◽  
Automated Production

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 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 Reducing the Costs of Automated Production Systems

2019 ◽  
Vol 23 (2) ◽  
pp. 44-47
Author(s):  
Konstantin Novikov ◽  
Pavel Vranek ◽  
Jana Kleinova ◽  
Michal Šimon
Keyword(s):  
Production Systems ◽  
Automated Production

Assessment of variance & distribution in data for effective use of statistical methods for product quality prediction

2018 ◽  
Vol 66 (4) ◽  
pp. 344-355 ◽  
Author(s):  
Iris Weiß ◽  
Birgit Vogel-Heuser
Keyword(s):  
Data Mining ◽  
Product Quality ◽  
Statistical Methods ◽  
Analysis Of Variance ◽  
Production System ◽  
Production Systems ◽  
High Potential ◽  
Quality Prediction ◽  
Automated Production ◽  
Effective Use

AbstractData mining in automated production systems provide high potential to increase the Overall Equipment Effectiveness. Nevertheless, data of such machines/plants include specific characteristics regarding the variance and distribution of the dataset. For modelling product quality prediction, these characteristics have to be analysed to interpret the results correctly. Therefore, an approach for the analysis of variance and distribution of datasets is proposed. The evaluation of this approach validates the developed guidelines, which identify the reasons for inconsistent prediction results based on two different datasets of the same production system.

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