Abstract
Structural runtime models provide a snapshot of the constituents of a system and their state. Capturing the history of runtime models, i.e., previous snapshots, has been shown to be useful for a number of aims. Handling, however, history at runtime poses important challenges to tool support. We present the InTempo tool which is based on the Eclipse Modeling Framework and encodes runtime models as graphs. Key features of InTempo, such as, the integration of temporal requirements into graph queries, the in-memory storage of the model, and a systematic method to contain the model’s memory consumption, intend to address issues which seemingly place limitations on the available tool support. InTempo offers two operation modes which support both runtime and postmortem application scenarios.
Chapter PDF
Similar content being viewed by others
References
Barkowsky, M., Giese, H.: Hybrid search plan generation for generalized graph pattern matching. JLAMP 114, 100563 (2020)
Bettini, L.: Implementing domain-specific languages with Xtext and Xtend. Packt Publishing Ltd (2016)
Bencomo N., Goetz S., and Song H.: Models@ run. time: a guided tour of the state of the art and research challenges. SoSyM 18.5 (2019)
Brun, Y., Di Marzo Serugendo, G., Gacek, C., Giese, et al.:Software engineering for self-adaptive systems, pp. 48–70. Heidelberg (2009) Springer
Búr, M., Szilágyi, G., Vörös, A., Varró, D.: Distributed graph queries over models@run.time for runtime monitoring of cyber-physical systems. STTT 22(1)
Ehrig, H., Prange, U., Taentzer, G.: Fundamental Theory for Typed Attributed Graph Transformation. ICGT Berlin, Heidelberg (2004) Springer
Eclipse Foundation: Eclipse modeling framework (EMF) (Aug 2020), https://www.eclipse.org/modeling/emf/, accessed: 2020-10-11
GarcÃa-DomÃnguez, A., Bencomo, N., Parra-Ullauri, J.M.,GarcÃa-Paucar, L.H.: Querying and Annotating Model Histories with Time-Aware Patterns. MODELS. pp. 194–204 (2019) ACM/IEEE
Ghahremani, S., Giese, H., Vogel, T.: Efficient utility-driven self-healing employing adaptation rules for large dynamic architectures. ICAC (2017)
Giese, H., Maximova, M., Sakizloglou, L., Schneider, S.: Metric Temporal Graph Logic over Typed Attributed Graphs. FASE, (2019) Springer
Gómez, A., Cabot, J., Wimmer, M.: TemporalEMF: A temporal metamodeling framework. ER, vol. 11157, pp. 365–381. (2018) Springer
Kleppe, A., Warmer, J.: An introduction to the object constraint language (OCL). In: TOOLS p. 456 (2000)
MDELab: InTempo Homepage, http://www.hpi.uni-potsdam.de/giese/public/mdelab/mdelab-projects/intempo/, accessed: 2021-01-19
Rhodes, A., Evans, L.E., Alhazzani, W., Levy, M.M., et al.: Surviving sepsis campaign: International guidelines for management of sepsis and septic shock: 2016. Intensive care medicine 43(3), 304–377 (2017)
Sakizloglou, L., Ghahremani, S., Barkowsky, M., Giese, H.: A scalable querying scheme for memory-efficient runtime models with history. MoDELS pp. 175–186. (2020) ACM/IEEE
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Open Access This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.
The images or other third party material in this chapter are included in the chapter's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.
Copyright information
© 2021 The Author(s)
About this paper
Cite this paper
Sakizloglou, L., Barkowsky, M., Giese, H. (2021). Keeping Pace with the History of Evolving Runtime Models. In: Guerra, E., Stoelinga, M. (eds) Fundamental Approaches to Software Engineering. FASE 2021. Lecture Notes in Computer Science(), vol 12649. Springer, Cham. https://doi.org/10.1007/978-3-030-71500-7_13
Download citation
DOI: https://doi.org/10.1007/978-3-030-71500-7_13
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-71499-4
Online ISBN: 978-3-030-71500-7
eBook Packages: Computer ScienceComputer Science (R0)