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Learning from Evolution for Evolution

Abstract

Successful system evolution is dependent on knowledge about the system itself, its past and its present, as well as the environment of the system. This chapter presents several approaches to automate the acquisition of knowledge about the system’s past, for example past evolution steps, and its present, for example models of its behaviour. Based on these results, further approaches support the validation and verification of evolution steps, as well as the recommendation of evolutions to the system, as well as similar systems. The approaches are illustrated using the joint automation production system case study, the Pick and Place Unit (PPU) and Extended Pick and Place Unit (xPPU).

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Correspondence to Matthias Tichy .

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Kögel, S. et al. (2019). Learning from Evolution for Evolution. In: Reussner, R., Goedicke, M., Hasselbring, W., Vogel-Heuser, B., Keim, J., Märtin, L. (eds) Managed Software Evolution. Springer, Cham. https://doi.org/10.1007/978-3-030-13499-0_10

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  • DOI: https://doi.org/10.1007/978-3-030-13499-0_10

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