PP 1593: ENsurance of Software evolUtion by Run-time cErtification II

Quality attributes play an important role in different classes of software systems, e.g. safety in embedded systems and performance in business information systems. Currently, quality requirements are typically checked at design time. For evolving systems with changing environmental conditions this leads to the problem that the system may behave differently with respect to quality attributes than analysed at design time. ENSURE proposes to address this problem by developing a holistic model-driven approach, which treats quality evaluation models as first class entities. This approach used dedicated model transformations to evolve quality evaluation models with structural and behavioural models. In the first phase, we developed a co-evolution approach for architectural as well as quality evaluation models which supports incremental change propagation between the models. This is complemented by an approach to efficiently learn the attributes of the quality evaluation models from the actual running system and an approach to specify the quality properties to analyse using controlled natural language. Complementary to these activities, we empirically studied model-driven engineering and its challenges related to our topics as well as how meta models of modelling languages evolve. We participated in both demonstrators, focusing on the Pick and Place Unit (PPU), and evaluated our approach on the PPU case study. In the second phase, we will extend our co-evolution approach by providing recommendation support for cases where the co-evolution specifications do not provide deterministic co-evolution using machines learning techniques on model histories. The second major extension is exploiting the information from the model changes, from the co-evolution for performance improvement of the quality analysis by an incremental approach. Finally, we will empirically study and evaluate the results from both phases with experts from industry as well as both demonstrators of the SPP. We will continue to be well integrated in the activities of the SPP.

Principal Investigators
Grunske, Lars Prof. Dr. (Details) (Software Engineering)

Duration of Project
Start date: 01/2016
End date: 08/2019

Research Areas
Software Engineering and Programming Languages

[4] Stefan Kögel, Matthias Tichy, Abhishek Chakraborty, Alexander Fay, Birgit Vogel-Heuser, Christopher Haubeck, Gabriele Taentzer, Timo Kehrer, Jan Ladiges, Lars Grunske, Mattias Ulbrich, Safa Bougouffa, Sinem Getir, Suhyun Cha, Udo Kelter, Winfried Lamersdorf, Kiana Busch, Robert Heinrich, Sandro Koch: Learning from Evolution for Evolution. Managed Software Evolution 2019: 255-308

[3] Sinem Getir, Lars Grunske, André van Hoorn, Timo Kehrer, Yannic Noller, Matthias Tichy: Supporting semi-automatic co-evolution of architecture and fault tree models. Journal of Systems and Software 142: 115-135 (2018)

[2] Sinem Getir, Esteban Pavese, Lars Grunske: Formal Semantics for Probabilistic Verification of Stochastic Regular Expressions. CS&P 2018

[1] Sinem Getir, Lars Grunske, Christian Karl Bernasko, Verena Käfer, Tim Sanwald, Matthias Tichy: CoWolf - A Generic Framework for Multi-view Co-evolution and Evaluation of Models. ICMT 2015: 34-40

Last updated on 2021-22-07 at 13:02