Owen Reynolds
Nelly Bencomo
ABSTRACT
Software systems are increasingly making decisions autonomously by incorporating AI and machine learning capabilities. These systems are known as self-adaptive and autonomous systems (SAS). Some of these decisions can have a life-changing impact on the people involved and therefore, they need to be appropriately tracked and justified: the system should not be taken as a black box. It is required to be able to have knowledge about past events and records of history of the decision making. However, tracking everything that was going on in the system at the time a decision was made may be unfeasible, due to resource constraints and complexity. In this paper, we propose an approach that combines the abstraction and reasoning support offered by models used at runtime with provenance graphs that capture the key decisions made by a system through its execution. Provenance graphs relate the entities, actors and activities that take place in the system over time, allowing for tracing the reasons why the system reached its current state. We introduce activity scopes, which highlight the high-level activities taking place for each decision, and reduce the cost of instrumenting a system to automatically produce provenance graphs of these decisions. We demonstrate a proof of concept implementation of our proposal across two case studies, and present a roadmap towards a reusable provenance layer based on the experiments.
- P. Arcaini, E. Riccobene, and P. Scandurra. 2015. Modeling and Analyzing MAPE-K Feedback Loops for Self-Adaptation. In Proceedings of SEAMS 2015. 13--23. Google Scholar
Digital Library
- Nelly Bencomo, Sebastian Götz, and Hui Song. 2019. [email protected]: a guided tour of the state of the art and research challenges. Software & Systems Modeling (2019). Google ScholarDigital Library
- Nelly Bencomo and Luis Hernán García Paucar. 2019. RaM: Causally-Connected and Requirements-Aware Runtime Models using Bayesian Learning. In Proc. of MODELS 2019. IEEE, 216--226. Google ScholarCross Ref
- Nelly Bencomo, Jon Whittle, Peter Sawyer, et al. 2010. Requirements reflection: requirements as runtime entities. In Proceedings of ICSE 2010. ACM, 199--202. Google ScholarDigital Library
- Gordon Blair, Nelly Bencomo, and Robert B. France. 2009. Models@ run.time. Computer 42, 10 (Oct 2009), 22--27. https://doi.org/10/bkpbtkGoogle ScholarDigital Library
- Petra Brosch, Gerti Kappel, Philip Langer, et al. 2012. An Introduction to Model Versioning. Vol. 7320. Springer Berlin Heidelberg, 336--398. Google ScholarDigital Library
- Kate Crawford, Roel Dobbe, Theodora Dryer, et al. 2019. AI Now 2019 Report. Technical Report. AI Now Institute. https://ainowinstitute.org/AI_Now_2019_Report.html Date last checked: February 28th, 2020.Google Scholar
- Eclipse Foundation. 2019. CDO Model Repository. https://www.eclipse.org/cdo/Date last checked: February 25th, 2020.Google Scholar
- Eclipse Foundation. 2020. EMF Compare homepage. https://www.eclipse.org/emf/compare/ Date last checked: February 25th, 2020.Google Scholar
- John Ellson, Emden Gansner, Yifan Hu, et al. 2020. Graphviz - Graph Visualization Software. https://graphviz.org/ Date last checked: August 26th, 2020.Google Scholar
- A. García-Domínguez, N. Bencomo, J. M. Parra Ullauri, and L. H. García Paucar. 2019. Querying and annotating model histories with time-aware patterns. In Proc. of MODELS 2019. IEEE, 194--204. Google ScholarCross Ref
- David Garlan and Bradley R. Schmerl. 2004. Using Architectural Models at Runtime: Research Challenges. In Proceedings of EWSA 2004 (LNCS, Vol. 3047). Springer, 200--205. Google ScholarCross Ref
- German Aerospace Center. 2019. SUMO homepage. http://sumo.sourceforge.net/ Date last checked: February 25th, 2020.Google Scholar
- Eleni Gessiou, Vasilis Pappas, Elias Athanasopoulos, et al. 2012. Towards a Universal Data Provenance Framework Using Dynamic Instrumentation. In Information Security and Privacy Research. Vol. 376. Springer Berlin Heidelberg, 103--114. Google ScholarCross Ref
- Holger Giese, Nelly Bencomo, Liliana Pasquale, et al. 2011. Living with Uncertainty in the Age of Runtime Models. In [email protected] - Foundations, Applications, and Roadmaps. Lecture Notes in Computer Science, Vol. 8378. Springer, 47--100. Google ScholarCross Ref
- Martin Gogolla, Lars Hamann, Frank Hilken, Mirco Kuhlmann, and Robert France. 2014. From Application Models to Filmstrip Models: An Approach to Automatic Validation of Model Dynamics. In Modellierung 2014. Gesellschaft für Informatik e.V., Bonn, 273--288.Google Scholar
- Paul Groth and Luc Moreau. 2013. PROV-Overview. Working Group Note. W3C. https://www.w3.org/TR/prov-overview/ Date last checked: February 25th, 2020.Google Scholar
- Thomas Hartmann, François Fouquet, Matthieu Jimenez, Romain Rouvoy, and Yves Le Traon. 2017. Analyzing Complex Data in Motion at Scale with Temporal Graphs. In Proceedings of SEKE 2017. Google ScholarCross Ref
- Melanie Herschel, Ralf Diestelkämper, and Houssem Ben Lahmar. 2017. A survey on provenance: What for? What form? What from? The VLDB Journal 26 (10 2017). Google ScholarDigital Library
- F. Hilken and M. Gogolla. 2016. Verifying Linear Temporal Logic Properties in UML/OCL Class Diagrams Using Filmstripping. In 2016 Euromicro Conference on Digital System Design (DSD). 708--713. Google ScholarCross Ref
- T. Costa Kohwalter, L. Gresta Paulino Murta, and E. Walter Gonzalez Clua. 2017. Capturing Game Telemetry with Provenance. In 2017 16th Brazilian Symposium on Computer Games and Digital Entertainment (SBGames). 66--75. Google ScholarCross Ref
- Luc Moreau, Ben Clifford, Juliana Freire, et al. 2011. The Open Provenance Model core specification (v1.1). Future Generation Computer Systems 27, 6 (Jun 2011), 743--756. Google ScholarDigital Library
- Brice Morin, Olivier Barais, Grégory Nain, and Jean-Marc Jézéquel. 2009. Taming Dynamically Adaptive Systems using models and aspects. In Proceedings of ICSE 2009. IEEE, 122--132. Google ScholarDigital Library
- Beatriz Pérez, Julio Rubio, and Carlos Sáenz-Adán. 2018. A systematic review of provenance systems. Knowledge and Information Systems 57, 3 (Dec 2018), 495--543. https://doi.org/10/gf8q84Google ScholarDigital Library
- Peter Sawyer, Nelly Bencomo, Jon Whittle, et al. 2010. Requirements-Aware Systems: A Research Agenda for RE for Self-adaptive Systems. In Proceedings of RE 2010. IEEE Computer Society, 95--103. Google ScholarDigital Library
- E. Seidewitz. 2003. What models mean. IEEE Software 20, 5 (2003), 26--32. Google ScholarDigital Library
- Daniel Seybold, Jörg Domaschka, Alessandro Rossini, et al. 2016. Experiences of models@run-time with EMF and CDO. In Proceedings of SLE 2016. ACM Press, 46--56. Google ScholarDigital Library
- Software Freedom Conservancy. 2020. Git project homepage. https://git-scm.com/ Date last checked: February 25th, 2020.Google Scholar
- David Steinberg, Frank Budinsky, Marcelo Paternostro, and Ed Merks. 2009. EMF: Eclipse Modeling Framework 2.0 (2nd ed.). Addison-Wesley Professional. ISBN: 978-0-321-33188-5.Google Scholar
- Unity Technologies. 2020. Unity project homepage. https://unity.com/frontpage Date last checked: February 25th, 2020.Google Scholar
- Kris Welsh, Nelly Bencomo, Pete Sawyer, and Jon Whittle. 2014. Self-Explanation in Adaptive Systems Based on Runtime Goal-Based Models. Springer, 122--145. Google ScholarCross Ref
Index Terms
- Automated provenance graphs for [email protected]
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