Prankur Agarwal, Shubham Sharma, Hafiyyan Fadhlillah, Rick Rabiser, Alois Zoitl,
"Delta Models as a Measurement for Improving the Quality of IEC 61499-based Control Software. 28th IEEE IES International Conference on Emerging Technologies and Factory Automation (ETFA 2023)"
: Proceedings of the 28th IEEE IES International Conference on Emerging Technologies and Factory Automation (ETFA 2023), IEEE, New York, NY, United States, Seite(n) 1-4, 10-2023, ISBN: 979-8-3503-3991-8
Original Titel:
Delta Models as a Measurement for Improving the Quality of IEC 61499-based Control Software. 28th IEEE IES International Conference on Emerging Technologies and Factory Automation (ETFA 2023)
Sprache des Titels:
Englisch
Original Buchtitel:
Proceedings of the 28th IEEE IES International Conference on Emerging Technologies and Factory Automation (ETFA 2023)
Original Kurzfassung:
Industrial-scale control software is designed as variability-intensive, i.e., highly configurable and adaptable software, to support diverse hardware capabilities and to fulfill diverse customer requirements. Various control software architectures have been proposed for developing highly configurable and adaptable control software. However, each architecture has its strengths and weaknesses in configurability and adaptability. Measuring the suitability of a given architecture to deal with variability can further guide control software engineers in implementing highly configurable and adaptable control software. In this paper, we propose measurement approaches for IEC 61499-based control software that indicate how well a particular architecture can manage variability. Control software engineers are first encouraged to describe all the functionalities in their system. They can also describe which elements must be added or removed when implementing a particular functionality. They then store these descriptions using a variability mechanism called delta models. Next, we can measure the size of delta models, the cohesion and coupling of the control software modules, and the degree of mapping complexity between features and delta models. We argue that by using our proposed measurements, we can indicate whether the control software?s architecture is suitable for variability-intensive software.
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