TY - GEN A1 - Brand, Thomas A1 - Giese, Holger T1 - Generic adaptive monitoring based on executed architecture runtime model queries and events T2 - IEEE Xplore N2 - Monitoring is a key functionality for automated decision making as it is performed by self-adaptive systems, too. Effective monitoring provides the relevant information on time. This can be achieved with exhaustive monitoring causing a high overhead consumption of economical and ecological resources. In contrast, our generic adaptive monitoring approach supports effectiveness with increased efficiency. Also, it adapts to changes regarding the information demand and the monitored system without additional configuration and software implementation effort. The approach observes the executions of runtime model queries and processes change events to determine the currently required monitoring configuration. In this paper we explicate different possibilities to use the approach and evaluate their characteristics regarding the phenomenon detection time and the monitoring effort. Our approach allows balancing between those two characteristics. This makes it an interesting option for the monitoring function of self-adaptive systems because for them usually very short-lived phenomena are not relevant. Y1 - 2019 SN - 978-1-7281-2731-6 U6 - https://doi.org/10.1109/SASO.2019.00012 SN - 1949-3673 SP - 17 EP - 22 PB - IEEE CY - New York ER - TY - GEN A1 - Ghahremani, Sona A1 - Giese, Holger T1 - Performance evaluation for self-healing systems BT - Current Practice & Open Issues T2 - 2019 IEEE 4th International Workshops on Foundations and Applications of Self* Systems (FAS*W) N2 - Evaluating the performance of self-adaptive systems (SAS) is challenging due to their complexity and interaction with the often highly dynamic environment. In the context of self-healing systems (SHS), employing simulators has been shown to be the most dominant means for performance evaluation. Simulating a SHS also requires realistic fault injection scenarios. We study the state of the practice for evaluating the performance of SHS by means of a systematic literature review. We present the current practice and point out that a more thorough and careful treatment in evaluating the performance of SHS is required. KW - self-healing KW - failure profile KW - evaluation KW - simulator KW - performance Y1 - 2019 SN - 978-1-7281-2406-3 U6 - https://doi.org/10.1109/FAS-W.2019.00039 SP - 116 EP - 119 PB - IEEE CY - New York ER - TY - JOUR A1 - Dyck, Johannes A1 - Giese, Holger A1 - Lambers, Leen T1 - Automatic verification of behavior preservation at the transformation level for relational model transformation JF - Software and systems modeling N2 - The correctness of model transformations is a crucial element for model-driven engineering of high-quality software. In particular, behavior preservation is an important correctness property avoiding the introduction of semantic errors during the model-driven engineering process. Behavior preservation verification techniques show some kind of behavioral equivalence or refinement between source and target model of the transformation. Automatic tool support is available for verifying behavior preservation at the instance level, i.e., for a given source and target model specified by the model transformation. However, until now there is no sound and automatic verification approach available at the transformation level, i.e., for all source and target models. In this article, we extend our results presented in earlier work (Giese and Lambers, in: Ehrig et al (eds) Graph transformations, Springer, Berlin, 2012) and outline a new transformation-level approach for the sound and automatic verification of behavior preservation captured by bisimulation resp.simulation for outplace model transformations specified by triple graph grammars and semantic definitions given by graph transformation rules. In particular, we first show how behavior preservation can be modeled in a symbolic manner at the transformation level and then describe that transformation-level verification of behavior preservation can be reduced to invariant checking of suitable conditions for graph transformations. We demonstrate that the resulting checking problem can be addressed by our own invariant checker for an example of a transformation between sequence charts and communicating automata. KW - Relational model transformation KW - Formal verification of behavior preservation KW - Behavioral equivalence and refinement KW - Bisimulation and simulation KW - Graph transformation KW - Triple graph grammars KW - Invariant checking Y1 - 2018 U6 - https://doi.org/10.1007/s10270-018-00706-9 SN - 1619-1366 SN - 1619-1374 VL - 18 IS - 5 SP - 2937 EP - 2972 PB - Springer CY - Heidelberg ER -