Supporting Semi-Automatic Co-Evolution of Architecture and Fault Tree Models

Published: 25 Apr 2018 | Version 1 | DOI: 10.17632/6kkh54xbpj.1
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Description of this data

In the whole life-cycle of systems in safety-critical domains, system models must consistently co-evolve with quality evaluation models like fault trees. However, performing these co-evolution steps is a cumbersome and often manual task. To understand this problem in detail, we have analyzed the evolution and mined common changes of architecture and fault tree models for a set of evolution scenarios of a part of a factory automation system called pick&place unit. Based on the results, we could derive a set of co-evolution rules which fully cover the evolution scenarios of the case study and which offer the potential to semi-automate the co-evolution process. In particular, we evaluated these rules by a comparison to typical visual editor operations. Our results show a signicant reduction of the amount of required user interactions in order to realize the co-evolution.

Experiment data files

Latest version

  • Version 1

    2018-04-25

    Published: 2018-04-25

    DOI: 10.17632/6kkh54xbpj.1

    Cite this dataset

    Getir, Sinem; Grunske, Lars ; van Hoorn, André; Kehrer, Timo; Noller, Yannic; Tichy, Matthias (2018), “Supporting Semi-Automatic Co-Evolution of Architecture and Fault Tree Models”, Mendeley Data, v1 http://dx.doi.org/10.17632/6kkh54xbpj.1

Categories

Architecture, Software, Metamodeling, Modelling, Fault

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CC BY 4.0 Learn more

The files associated with this dataset are licensed under a Creative Commons Attribution 4.0 International licence.

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This dataset is licensed under a Creative Commons Attribution 4.0 International licence. What does this mean? You can share, copy and modify this dataset so long as you give appropriate credit, provide a link to the CC BY license, and indicate if changes were made, but you may not do so in a way that suggests the rights holder has endorsed you or your use of the dataset. Note that further permission may be required for any content within the dataset that is identified as belonging to a third party.

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