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Software and Systems Modeling

Erschienen in:

09.01.2019 | Regular Paper

Models@run.time: a guided tour of the state of the art and research challenges

verfasst von: Nelly Bencomo, Sebastian Götz, Hui Song

Erschienen in: Software and Systems Modeling | Ausgabe 5/2019

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Abstract

More than a decade ago, the research topic models@run.time was coined. Since then, the research area has received increasing attention. Given the prolific results during these years, the current outcomes need to be sorted and classified. Furthermore, many gaps need to be categorized in order to further develop the research topic by experts of the research area but also newcomers. Accordingly, the paper discusses the principles and requirements of models@run.time and the state of the art of the research line. To make the discussion more concrete, a taxonomy is defined and used to compare the main approaches and research outcomes in the area during the last decade and including ancestor research initiatives. We identified and classified 275 papers on models@run.time, which allowed us to identify the underlying research gaps and to elaborate on the corresponding research challenges. Finally, we also facilitate sustainability of the survey over time by offering tool support to add, correct and visualize data.

Vorheriger Artikel Student experience with software modeling tools

Nächster Artikel Empirical study on the effectiveness and efficiency of model-driven architecture techniques

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https://github.com/sebastiangoetz/slr-toolkit/releases.

https://github.com/sebastiangoetz/slr-toolkit/tree/master/examples/mrt.

https://github.com/sebastiangoetz/slr-toolkit.

Self-optimizing systems are a special subclass of self-adaptive systems [151]. Approaches of this class are not included in class self-adaptation to enable a separate investigation. Other subclasses of self-adaptive systems did not reveal to be significant.

https://github.com/sebastiangoetz/slr-toolkit.

Most of the 56 approaches classified as fundamental work would else be shown as dominating axes in the bubble matrix charts, which distracts from the investigation of applied approaches.

https://github.com/sebastiangoetz/slr-toolkit/tree/master/examples/mrt.

Abeywickrama, D.B., Serbedzija, N., Loreti, M.: Monitoring and visualizing adaptation of autonomic systems at runtime. In: Proceedings of the 30th Annual ACM Symposium on Applied Computing, SAC ’15, pp. 1857–1860. ACM, New York, NY, USA (2015). https://doi.org/10.1145/2695664.2695983

Albassam, E., Porter, J., Gomaa, H., Menasci, D.A.: Dare: a distributed adaptation and failure recovery framework for software systems. In: 2017 IEEE International Conference on Autonomic Computing (ICAC), pp. 203–208 (2017). https://doi.org/10.1109/ICAC.2017.12

Alfarez, G., Pelechano, V., Mazo, R., Salinesi, C., Diaz, D.: Dynamic adaptation of service compositions with variability models. J. Syst. Softw. 91, 24–47 (2014). https://doi.org/10.1016/j.jss.2013.06.034 CrossRef

Almorsy, M., Grundy, J., Ibrahim, A.S.: Adaptable, model-driven security engineering for SaaS cloud-based applications. Autom. Softw. Eng. 21(2), 187–224 (2014)CrossRef

Al-Refai, M., Cazzola, W., France, R.: Using models to dynamically refactor runtime code. In: Proceedings of the 29th Annual ACM Symposium on Applied Computing, SAC ’14, pp. 1108–1113. ACM, New York, NY, USA (2014). https://doi.org/10.1145/2554850.2554954

Amoui, M., Derakhshanmanesh, M., Ebert, J., Tahvildari, L.: Achieving dynamic adaptation via management and interpretation of runtime models. J. Syst. Softw. 85(12), 2720–2737 (2012). https://doi.org/10.1016/j.jss.2012.05.033 CrossRef

Anaya, I.D.P., Simko, V., Bourcier, J., Plouzeau, N., Jézéquel, J.M.: A prediction-driven adaptation approach for self-adaptive sensor networks. In: Proceedings of the 9th International Symposium on Software Engineering for Adaptive and Self-Managing Systems, SEAMS 2014, pp. 145–154. ACM, New York, NY, USA (2014). https://doi.org/10.1145/2593929.2593941

Anderson, S., Bredeche, N., Eiben, A., Kampis, G., van Steen, M.: Adaptive Collective Systems: Herding Black Sheep. Bookprints, Minneapolis (2013)

Andersson, J., Ericsson, M., Löwe, W.: Automatic rule derivation for adaptive architectures. In: 7th Working IEEE/IFIP Conference on Software Architecture, pp. 323–326. IEEE (2008)

10.

Andersson, J., Lemos, R., Malek, S., Weyns, D. (2009) Modeling dimensions of self-adaptive software systems. In: Cheng B.H., Lemos R., Giese H., Inverardi P., Magee J. (eds.) Software Engineering for Self-Adaptive Systems, Chap. Modeling Dimensions of Self-Adaptive Software Systems, pp. 27–47. Springer, Berlin. https://doi.org/10.1007/978-3-642-02161-9_2

11.

Anthony, R., Pelc, M., Ward, P., Hawthorne, J., Pulnah, K.: A run-time configurable software architecture for self-managing systems. In: International Conference on Autonomic Computing, 2008. ICAC ’08, pp. 207–208 (2008). https://doi.org/10.1109/ICAC.2008.23

12.

Arcaini, P., Riccobene, E., Scandurra, P.: Modeling and analyzing MAPE-K feedback loops for self-adaptation. In: Proceedings of the 10th International Symposium on Software Engineering for Adaptive and Self-Managing Systems, SEAMS ’15, pp. 13–23. IEEE Press, Piscataway, NJ, USA (2015). http://dl.acm.org/citation.cfm?id=2821357.2821362

13.

Arcega, L., Font, J., Haugen, Ø., Cetina, C.: An infrastructure for generating run-time model traces for maintenance tasks. In: Proceedings of the 11th International Workshop on Models@run.time co-located with 19th International Conference on Model Driven Engineering Languages and Systems (MODELS 2016), Saint Malo, France, 4 October 2016, pp. 35–42 (2016). http://ceur-ws.org/Vol-1742/MRT16_paper_7.pdf

14.

Arias, T.B.C., America, P., Avgeriou, P.: Defining execution viewpoints for a large and complex software-intensive system. In: Joint Working IEEE/IFIP Conference on Software Architecture, 2009 and European Conference on Software Architecture. WICSA/ECSA 2009, pp. 1–10. IEEE (2009). (They never use the term “models@runtime”, nor cite our paper, but it is essentially the same idea)

15.

Barbier, F., Cariou, E., Le Goaer, O., Pierre, S.: Software adaptation: classification and a case study with state chart xml. IEEE Softw. 32(5), 68–76 (2015)CrossRef

16.

Baresi, L., Ghezzi, C.: The disappearing boundary between development-time and run-time. In: Proceedings of the FSE/SDP Workshop on Future of Software Engineering Research, FoSER ’10, pp. 17–22. ACM, New York, NY, USA (2010). https://doi.org/10.1145/1882362.1882367

17.

Baresi, L., Pasquale, L., Spoletini, P.: Fuzzy goals for requirements-driven adaptation. In: RE 2010, 18th IEEE International Requirements Engineering Conference, Sydney, New South Wales, Australia, 27 September–1 October 2010, pp. 125–134 (2010). http://dx.doi.org/10.1109/RE.2010.25

18.

Baresi, L., Pasquale, L.: Live goals for adaptive service compositions. In: Proceedings of the 2010 ICSE Workshop on Software Engineering for Adaptive and Self-Managing Systems, SEAMS ’10, pp. 114–123. ACM, New York, NY, USA (2010). https://doi.org/10.1145/1808984.1808997

19.

Baresi, L.: Self-adaptive systems, services, and product lines. In: Proceedings of the 18th International Software Product Line Conference—Volume 1, SPLC ’14, pp. 2–4. ACM, New York, NY, USA (2014). https://doi.org/10.1145/2648511.2648512

20.

Baxter, I.: Keynote: supporting forward and reverse engineering with multiple types of models. In: Proceedings of the 20th International Conference on Model-driven Engineering, Systems and Languages. IEEE (2017)

21.

Bellman, K.L., Landauer, C., Nelson, P., Bencomo, N., Götz, S., Lewis, P., Esterle, L.: Self-Modeling and Self-Awareness, pp. 279–304. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-47474-8_9

22.

Bencomo, N., Belaggoun, A., Issarny, V.: Dynamic decision networks for decision-making in self-adaptive systems: a case study. In: Proceedings of the 8th International Symposium on Software Engineering for Adaptive and Self-Managing Systems, SEAMS ’13, pp. 113–122. IEEE Press, Piscataway, NJ, USA (2013). http://dl.acm.org/citation.cfm?id=2487336.2487355

23.

Bencomo, N., Belaggoun, A., Issarny, V.: Dynamic decision networks for decision-making in self-adaptive systems: a case study. In: Proceedings of the 8th International Symposium on Software Engineering for Adaptive and Self-Managing Systems, SEAMS 2013, San Francisco, CA, USA, 20–21 May 2013, pp. 113–122 (2013). https://doi.org/10.1109/SEAMS.2013.6595498

24.

Bencomo, N., Grace, P., Flores-Cortés, C.A., Hughes, D., Blair, G.S.: Genie: supporting the model driven development of reflective, component-based adaptive systems. In: 30th International Conference on Software Engineering (ICSE 2008), Leipzig, Germany, 10–18 May 2008, pp. 811–814 (2008). https://doi.org/10.1145/1368088.1368207

25.

Bencomo, N., Whittle, J., Sawyer, P., Finkelstein, A., Letier, E.: Requirements reflection: requirements as runtime entities. In: 2010 ACM/IEEE 32nd International Conference on Software Engineering, vol. 2, pp. 199–202 (2010). https://doi.org/10.1145/1810295.1810329

26.

Bencomo, N.: The role of models@run.time in autonomic systems: keynote. In: 2017 IEEE International Conference on Autonomic Computing, ICAC 2017, Columbus, OH, USA, 17–21 July 2017, pp. 293–294 (2017). https://doi.org/10.1109/ICAC.2017.55

27.

Bencomo, N., Belaggoun, A.: Supporting Decision-Making for Self-Adaptive Systems: From Goal Models to Dynamic Decision Networks, pp. 221–236. Springer, Berlin (2013). https://doi.org/10.1007/978-3-642-37422-7_16

28.

Bencomo, N., Bennaceur, A., Grace, P., Blair, G., Issarny, V.: The role of models@run.time in supporting on-the-fly interoperability. Computing 95(3), 167–190 (2012)CrossRef

29.

Bencomo, N., Hallsteinsen, S., De Almeida, E.S.: A view of the dynamic software product line landscape. Computer 45(10), 36–41 (2012). https://doi.org/10.1109/MC.2012.292 CrossRef

30.

Bencomo, N., France, R., Cheng, B.H.C., Aßmann, U.: Models@run.time. Foundations, Applications, and Roadmaps, vol. 8378. Springer, Cham (2014)CrossRef

31.

Bencomo, N., Torres, R., Salas, R., Astudillo, H.: An architecture based on computing with words to support runtime reconfiguration decisions of service-based systems. Int. J. Comput. Intell. Syst. 11(1), 272–281 (2018). (Copyright 2018, the Authors. Published by Atlantis Press. This is an open access article under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/). Funding: UNAB Grant DI-1303-16/RG, grant FONDEF IDeA ID16I10322, FONDECYT Grant 1140408)

32.

Bennaceur, A., France, R.B., Tamburrelli, G., Vogel, T., Mosterman, P.J., Cazzola, W., Costa, F.M., Pierantonio, A., Tichy, M., Aksit, M., Emmanuelson, P., Huang, G., Georgantas, N., Redlich, D.: Mechanisms for leveraging models at runtime in self-adaptive software. In: Models@run.time—Foundations, Applications, and Roadmaps (Dagstuhl Seminar 11481, 27 November–2 December 2011), pp. 19–46 (2014). https://doi.org/10.1007/978-3-319-08915-7_2

33.

Bennaceur, A., Issarny, V.: Automated synthesis of mediators to support component interoperability. IEEE Trans. Softw. Eng. 41, 221–240 (2015)CrossRef

34.

Bézivin, J., Jouault, F., Valduriez, P.: On the need for megamodels. In: Proceedings of the OOPSLA/GPCE: Best Practices for Model-Driven Software Development Workshop, 19th Annual ACM Conference on Object-Oriented Programming, Systems, Languages, and Applications, Vancouver, Canada (2004). https://hal.archives-ouvertes.fr/hal-01222947

35.

Blair, G., Bencomo, N., France, R.: Models@run.time. Computer 42(10), 22–27 (2009). https://doi.org/10.1109/MC.2009.326 CrossRef

36.

Bosch, J.: Delivering customer value in the age of autonomous, continuously evolving systems. In: 2016 IEEE 24th International Requirements Engineering Conference (RE), pp. 1–1 (2016). https://doi.org/10.1109/RE.2016.16

37.

Calinescu, R., France, R., Ghezzi, C.: Models@run.time. Computer 95(3), 165–166 (2013)CrossRef

38.

Calinescu, R., France, R.B., Ghezzi, C.: Editorial. Computing 95(3), 165–166 (2013). https://doi.org/10.1007/s00607-012-0238-4 CrossRefMATH

39.

Cámara, J., Correia, P., De Lemos, R., Garlan, D., Gomes, P., Schmerl, B., Ventura, R.: Evolving an adaptive industrial software system to use architecture-based self-adaptation. In: Proceedings of the 8th International Symposium on Software Engineering for Adaptive and Self-Managing Systems, SEAMS ’13, pp. 13–22. IEEE Press, Piscataway, NJ, USA (2013)

40.

Cámara, J., Bellman, K.L., Kephart, J.O., Autili, M., Bencomo, N., Diaconescu, A., Giese, H., Götz, S., Inverardi, P., Kounev, S., Tivoli, M.: Self-Aware Computing Systems: Related Concepts and Research Areas, pp. 17–49. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-47474-8_2 CrossRef

41.

Capilla, R., Bosch, J.: The promise and challenge of runtime variability. Computer 44(12), 93–95 (2011). https://doi.org/10.1109/MC.2011.382 CrossRef

42.

Castañeda, L., Villegas, N.M., Müller, H.A.: Self-adaptive applications: on the development of personalized web-tasking systems. In: Proceedings of the 9th International Symposium on Software Engineering for Adaptive and Self-Managing Systems, SEAMS 2014, pp. 49–54. ACM, New York, NY, USA (2014). https://doi.org/10.1145/2593929.2593942

43.

Cazzola, W., Rossini, N.A., Bennett, P., Mandalaparty, S.P., France, R.B.: Fine-grained semi-automated runtime evolution. In: Models@run.time—Foundations, Applications, and Roadmaps (Dagstuhl Seminar 11481, 27 November–2 December 2011), pp. 237–258 (2014). https://doi.org/10.1007/978-3-319-08915-7_9

44.

Cazzola, W., Rossini, N.A., Al-Refai, M., France, R.B.: Fine-Grained Software Evolution Using UML Activity and Class Models, pp. 271–286. Springer, Berlin (2013). https://doi.org/10.1007/978-3-642-41533-3_17

45.

Cetina, C., Giner, P., Fons, J., Pelechano, V.: A model-driven approach for developing self-adaptive pervasive systems. In: Proceedings of the 3rd International Models@ Runtime Workshop, pp. 97–106 (2008)

46.

Cetina, C., Giner, P., Fons, J., Pelechano, V.: Autonomic computing through reuse of variability models at runtime: the case of smart homes. Computer 42(10), 37–43 (2009)CrossRef

47.

Chen, B., Peng, X., Yu, Y., Nuseibeh, B., Zhao, W.: Self-adaptation through incremental generative model transformations at runtime. In: 36th International Conference on Software Engineering, ICSE ’14, Hyderabad, India—31 May–07 June 2014, pp. 676–687 (2014). https://doi.org/10.1145/2568225.2568310

48.

Chen, T., Bahsoon, R.: Self-adaptive and online qos modeling for cloud-based software services. IEEE Trans. Softw. Eng. 43(5), 453–475 (2017). https://doi.org/10.1109/TSE.2016.2608826 CrossRef

49.

Chen, X., Li, A., Zeng, X., Guo, W., Huang, G.: Runtime model based approach to iot application development. Front. Comput. Sci. 9(4), 540–553 (2015)CrossRef

50.

Cheng, B.H.C., Eder, K.I., Gogolla, M., Grunske, L., Litoiu, M., Müller, H.A., Pelliccione, P., Perini, A., Qureshi, N.A., Rumpe, B., Schneider, D., Trollmann, F., Villegas, N.M.: Using models at runtime to address assurance for self-adaptive systems. In: Models@run.time—Foundations, Applications, and Roadmaps (Dagstuhl Seminar 11481, 27 November–2 December 2011), pp. 101–136 (2011). https://doi.org/10.1007/978-3-319-08915-7_4

51.

52.

Combemale, B., Broto, L., Crégut, X., Daydé, M., Hagimont, D.: Autonomic management policy specification: from uml to dsml. In: Model Driven Engineering Languages and Systems, pp. 584–599. Springer (2008)

53.

Criado, J., Vicente-Chicote, C., Padilla, N., Iribarne, L.: A model-driven approach to graphical user interface runtime adaptation. In: Proceedings of the 5th Workshop on Models@run.time, pp. 49–59 (2010)

54.

Dávid, I., Ráth, I., Varró, D.: Foundations for streaming model transformations by complex event processing. Softw. Syst. Model. (2016). https://doi.org/10.1007/s10270-016-0533-1

55.

de Grandis, P., Valetto, G.: Elicitation and utilization of application-level utility functions. In: Proceedings of the 6th International Conference on Autonomic Computing, pp. 107–116. ACM (2009). https://doi.org/10.1145/1555228.1555259

56.

de Lemos, R., Giese, H., Müller, H.A., Shaw, M., Andersson, J., Litoiu, M., Schmerl, B., Tamura, G., Villegas, N.M., Vogel, T., Weyns, D., Baresi, L., Becker, B., Bencomo, N., Brun, Y., Cukic, B., Desmarais, R., Dustdar, S., Engels, G., Geihs, K., Göschka, K.M., Gorla, A., Grassi, V., Inverardi, P., Karsai, G., Kramer, J., Lopes, A., Magee, J., Malek, S., Mankovskii, S., Mirandola, R., Mylopoulos, J., Nierstrasz, O., Pezzè, M., Prehofer, C., Schäfer, W., Schlichting, R., Smith, D.B., Sousa, J.P., Tahvildari, L., Wong, K., Wuttke, J.: Software Engineering for Self-Adaptive Systems: A Second Research Roadmap, pp. 1–32. Springer, Berlin (2013). https://doi.org/10.1007/978-3-642-35813-5_1

57.

De Oliveira Filho, J., Papp, Z., Djapic, R., Oosteveen, J.: Model-based design of self-adapting networked signal processing systems. In: IEEE 7th International Conference on Self-Adaptive and Self-Organizing Systems (SASO), 2013, pp. 41–50 (2013). https://doi.org/10.1109/SASO.2013.16

58.

Debbabi, B., Diaconescu, A., Lalanda, P.: Controlling self-organising software applications with archetypes. In: IEEE 6th International Conference on Self-Adaptive and Self-Organizing Systems (SASO), 2012, pp. 69–78 (2012). https://doi.org/10.1109/SASO.2012.21

59.

DeLoach, S.A., Ou, X., Zhuang, R., Zhang, S.: Model-driven, moving-target defense for enterprise network security. In: Models@run.time—Foundations, Applications, and Roadmaps (Dagstuhl Seminar 11481, 27 November–2 December 2011), pp. 137–161 (2014). https://doi.org/10.1007/978-3-319-08915-7_5

60.

Denker, M., Ressia, J., Greevy, O., Nierstrasz, O.: Modeling features at runtime. In: Model-Driven Engineering Languages and Systems, pp. 138–152. Springer (2010)

61.

Derakhshanmanesh, M., Amoui, M., O’Grady, G., Ebert, J., Tahvildari, L.: Graf: graph-based runtime adaptation framework. In: Proceedings of the 6th International Symposium on Software Engineering for Adaptive and Self-Managing Systems, SEAMS ’11, pp. 128–137. ACM, New York, NY, USA (2011). https://doi.org/10.1145/1988008.1988026

62.

Derakhshanmanesh, M., Grieger, M., Ebert, J.: On the need for extended transactional models@run.time. In: Götz, S., Bencomo, N., Blair, G., Song, H. (eds.) Proceedings of the 10th International Workshop on Models@run.time, pp. 21–30. CEUR-WS.org (2015)

63.

Devries, B., Cheng, B.: Using models at run time to detect incomplete and inconsistent requirements. In: Proceedings of the 12th International Workshop on Models@run.time Co-located with 20th International Conference on Model Driven Engineering Languages and Systems (MODELS 2016), 19 September 2017, Austin, TX, USA (2017)

64.

Diaconescu, A., Bellman, K.L., Esterle, L., Giese, H., Götz, S., Lewis, P., Zisman, A.: Architectures for Collective Self-Aware Computing Systems, pp. 191–235. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-47474-8_7 CrossRef

65.

Didona, D., Romano, P., Peluso, S., Quaglia, F.: Transactional auto scaler: elastic scaling of in-memory transactional data grids. In: Proceedings of the 9th International Conference on Autonomic Computing, pp. 125–134. ACM (2012). https://doi.org/10.1145/2371536.2371559

66.

Ding, Y., Namatame, N., Riedel, T., Miyaki, T., Budde, M.: Smartteco: context-based ambient sensing and monitoring for optimizing energy consumption. In: Proceedings of the 8th ACM International Conference on Autonomic Computing, pp. 169–170. ACM (2011). https://doi.org/10.1145/1998582.1998612

67.

Ebraert, P., Tourwe, T.: A reflective approach to dynamic software evolution. In: Cazzola, W., Chiba, S., Saake, G. (eds.) Research Report C-196, pp. 37–43. Department of Mathematical and Computing Sciences, Tokyo Institute of Technology, Tokyo (2004)

68.

El Kateb, D., Zannone, N., Moawad, A., Caire, P., Nain, G., Mouelhi, T., Le Traon, Y.: Conviviality-driven access control policy. Requir. Eng. 20(4), 363–382 (2015). https://doi.org/10.1007/s00766-014-0204-0 CrossRef

69.

Elkhodr, M., Shahrestani, S.A., Cheung, H.: The Internet of Things: new interoperability, management and security challenges. CoRR arXiv:1604.04824 (2016)

70.

Esfahani, N., Malek, S.: Uncertainty in Self-Adaptive Software Systems, pp. 214–238. Springer, Berlin (2013). https://doi.org/10.1007/978-3-642-35813-5_9 CrossRef

71.

Esfahani, N., Yuan, E., Canavera, K.R., Malek, S.: Inferring software component interaction dependencies for adaptation support. ACM Trans. Auton. Adapt. Syst. 10, 26:1–26:32 (2016)CrossRef

72.

Evesti, A., Ovaska, E.: Ontology-based security adaptation at run-time. In: 4th IEEE International Conference on Self-Adaptive and Self-Organizing Systems (SASO), 2010, pp. 204–212 (2010). https://doi.org/10.1109/SASO.2010.11

73.

Ferry, N., Hourdin, V., Lavirotte, S., Rey, G., Tigli, J.Y., Riveill, M.: Models at runtime: service for device composition and adaptation. In: Proceedings of the 4th Workshop on Models@run.time, pp. 51–60 (2009)

74.

Fiadeiro, J.L., Lopes, A.: A model for dynamic reconfiguration in service-oriented architectures. In: Proceedings of 4th European Conference on Software Architecture, ECSA 2010, Copenhagen, Denmark, 23–26 August 2010, pp. 70–85 (2010). https://doi.org/10.1007/978-3-642-15114-9_8

75.

Filho, R.R., Porter, B.: Defining emergent software using continuous self-assembly, perception, and learning. ACM Trans. Auton. Adapt. Syst. 12(3), 16:1–16:25 (2017). https://doi.org/10.1145/3092691 CrossRef

76.

Filieri, A., Ghezzi, C., Grassi, V., Mirandola, R.: Reliability analysis of component-based systems with multiple failure modes. In: Proceedings of 13th International Symposium on Component-Based Software Engineering, CBSE 2010, Prague, Czech Republic, 23–25 June 2010, pp. 1–20 (2010). https://doi.org/10.1007/978-3-642-13238-4_1

77.

Filieri, A., Tamburrelli, G., Ghezzi, C.: Supporting self-adaptation via quantitative verification and sensitivity analysis at run time. IEEE Trans. Softw. Eng. 42(1), 75–99 (2016). https://doi.org/10.1109/TSE.2015.2421318 CrossRef

78.

Fleurey, F., Dehlen, V., Bencomo, N., Morin, B., Jezequel, J.M.: Modeling and validating dynamic adaptation. In: Proceedings of the 3rd International Models@ Runtime Workshop, pp. 36–46 (2008)

79.

Fouquet, F., Morin, B., Fleurey, F., Barais, O., Plouzeau, N., Jézéquel, J.: A dynamic component model for cyber physical systems. In: Proceedings of the 15th ACM SIGSOFT Symposium on Component Based Software Engineering, CBSE 2012, Part of Comparch ’12 Federated Events on Component-Based Software Engineering and Software Architecture, Bertinoro, Italy, 25–28 June 2012, pp. 135–144 (2012). https://doi.org/10.1145/2304736.2304759

80.

Fouquet, F., Nain, G., Morin, B., Daubert, E., Barais, O., Plouzeau, N., Jézéquel, J.M.: An eclipse modelling framework alternative to meet the models@ runtime requirements. In: Proceedings of the 15th International Conference on Model Driven Engineering Languages and Systems, pp. 87–101. Springer (2012)

81.

France, R., Rumpe, B.: Model-driven development of complex software: a research roadmap. In: Briand, L., Wolf, A. (eds.) Future of Software Engineering. IEEE-CS Press, Piscataway (2007)

82.

Gamez, N., Fuentes, L., Troya, J.: Creating self-adapting mobile systems with dynamic software product lines. IEEE Softw. 32(2), 105–112 (2015)CrossRef

83.

Garcia, A., Bencomo, N.: Non-human modelers: Can they work? In: Proceedings of Workshops, STAF 2017, Software Technologies: Applications and Foundations (2017)

84.

Garlan, D., Schmerl, B.: Using Architectural Models at Runtime: Research Challenges. Springer, Berlin (2004)

85.

Georgas, J.C., van der Hoek, A., Taylor, R.N.: Using architectural models to manage and visualize runtime adaptation. Computer 42(10), 0052–60 (2009)CrossRef

86.

Gerbert-Gaillard, E., Lalanda, P.: Self-aware model-driven pervasive systems. In: 2016 IEEE International Conference on Autonomic Computing (ICAC), pp. 221–222 (2016). https://doi.org/10.1109/ICAC.2016.26

87.

Ghahremani, S., Giese, H., Vogel, T.: Efficient utility-driven self-healing employing adaptation rules for large dynamic architectures. In: 2017 IEEE International Conference on Autonomic Computing (ICAC), pp. 59–68 (2017). https://doi.org/10.1109/ICAC.2017.35

88.

Ghezzi, C., Mocci, A., Sangiorgio, M.: Runtime monitoring of component changes with spy@runtime. In: Proceedings of the 34th International Conference on Software Engineering, ICSE ’12, pp. 1403–1406. IEEE Press, Piscataway, NJ, USA (2012). http://dl.acm.org/citation.cfm?id=2337223.2337430

89.

Gjerlufsen, T., Ingstrup, M., Olsen, J.W.: Mirrors of meaning: supporting inspectable runtime models. Computer 42(10), 61–68 (2009). (This paper is focused on the reflection of programs’ runtime status)CrossRef

90.

Gonzalez-Herrera, I., Bourcier, J., Daubert, E., Rudametkin, W., Barais, O., Fouquet, F., Jézéquel, J.M.: Scapegoat: an adaptive monitoring framework for component-based systems. In: IEEE/IFIP Conference on Software Architecture (WICSA), 2014, pp. 67–76. IEEE (2014)

91.

Götz, S., Gerostathopoulos, I., Krikava, F., Shahzada, A., Spalazzese, R.: Adaptive exchange of distributed partial models@run.time for highly dynamic systems. In: Proceedings of the 10th International Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS). IEEE (2015)

92.

Götz, S., Kühn, T.: Models@run.time for object-relational mapping supporting schema evolution. In: Götz, S., Bencomo, N., Blair, G., Song, H. (eds.) Proceedings of the 10th International Workshop on Models@run.time, pp. 41–50. CEUR-WS.org (2015)

93.

Götz, S., Schöne, R., Wilke, C., Mendez, J., Assmann, U.: Towards predictive self-optimization by situation recognition. In: Proceedings of 2nd Workshop “Energy Aware Software—Engineering and Development” (EASED) (2013)

94.

Götz, S.: Supporting systematic literature reviews in computer science: the systematic literature review toolkit. In: MoDELS Companion, pp. 22–26. ACM (2018)

95.

Götz, S., Bencomo, N., France, R.B.: Devising the future of the models@run.time workshop. ACM SIGSOFT Softw. Eng. Notes 40(1), 26–29 (2015). https://doi.org/10.1145/2693208.2693249 CrossRef

96.

Grohmann, J., Herbst, N., Spinner, S., Kounev, S.: Self-tuning resource demand estimation. In: 2017 IEEE International Conference on Autonomic Computing (ICAC), pp. 21–26 (2017). https://doi.org/10.1109/ICAC.2017.19

97.

Guo, T., Shenoy, P.: Model-driven geo-elasticity in database clouds. In: 2015 IEEE International Conference on Autonomic Computing (ICAC), pp. 61–70 (2015). https://doi.org/10.1109/ICAC.2015.46

98.

Hallsteinsen, S., Hinchey, M., Park, S., Schmid, K.: Dynamic software product lines. Computer 41(4), 93–95 (2008). https://doi.org/10.1109/MC.2008.123 CrossRef

99.

Hartmann, T., Moawad, A., Fouquet, F., Le Traon, Y.: The next evolution of MDE: a seamless integration of machine learning into domain modeling. Softw. Syst. Model. (2017). https://doi.org/10.1007/s10270-017-0600-2

100.

Hartmann, T., Moawad, A., Fouquet, F., Nain, G., Klein, J., Traon, Y.L.: Stream my models: Reactive peer-to-peer distributed models@run.time. In: ACM/IEEE 18th International Conference on Model Driven Engineering Languages and Systems (MODELS), 2015, pp. 80–89 (2015). https://doi.org/10.1109/MODELS.2015.7338238

101.

Heinzemann, C., Becker, S., Volk, A.: Transactional execution of hierarchical reconfigurations in cyber-physical systems. Softw. Syst. Model. (2017). https://doi.org/10.1007/s10270-017-0583-z

102.

Hinchey, M., Park, S., Schmid, K.: Building dynamic software product lines. Computer 45, 22–26 (2012). https://doi.org/10.1109/MC.2012.332 CrossRef

103.

Hong, Jy, Suh, Eh, Kim, S.J.: Context-aware systems. Expert Syst. Appl. 36(4), 8509–8522 (2009). https://doi.org/10.1016/j.eswa.2008.10.071 CrossRef

104.

Hooman, J., Hendriks, T.: Model-based run-time error detection. In: Giese, H. (ed.) Models in Software Engineering, Lecture Notes in Computer Science, vol. 5002, pp. 225–236. Springer, Berlin (2008). https://doi.org/10.1007/978-3-540-69073-3_24

105.

Hussein, M., Han, J., Yu, J., Colman, A.: Enabling runtime evolution of context-aware adaptive services. In: 2013 IEEE International Conference on Services Computing, pp. 248–255 (2013). https://doi.org/10.1109/SCC.2013.77

106.

Iordanov, B., Alexandrova, A., Abbas, S., Hilpold, T., Upadrasta, P.: The semantic web as a software modeling tool: an application to citizen relationship management. In: Model-Driven Engineering Languages and Systems, pp. 589–603. Springer (2013)

107.

Jacques-Silva, G., Challenger, J., Degenaro, L., Giles, J., Wagle, R.: Towards autonomic fault recovery in system-s. In: 4th International Conference on Autonomic Computing, 2007. ICAC ’07, pp. 31–31 (2007). https://doi.org/10.1109/ICAC.2007.40

108.

Janik, A., Zielinski, K.: Transparent resource management and self-adaptability using multitasking virtual machine RM API. In: Proceedings of the 2006 International Workshop on Self-Adaptation and Self-Managing Systems, SEAMS ’06, pp. 51–57. ACM, New York, NY, USA (2006). https://doi.org/10.1145/1137677.1137688

109.

Javed, F., Arshad, N.: Adopt: an adaptive optimization framework for large-scale power distribution systems. In: 3rd IEEE International Conference on Self-Adaptive and Self-Organizing Systems, 2009. SASO ’09, pp. 254–264 (2009). https://doi.org/10.1109/SASO.2009.26

110.

Johanndeiter, T., Goldstein, A., Frank, U.: Towards business process models at runtime. In: Proceedings of the 8th Workshop on Models@run.time, pp. 13–25. CEUR-WS.org (2013)

111.

Junior, A.S., Costa, F., Clarke, P.: A model-driven approach to develop and manage cyber-physical systems. In: Proceedings of the 8th Workshop on Models@run.time, pp. 62–73. CEUR-WS.org (2013)

112.

Karol, S., Bürger, C., Aßmann, U.: Towards well-formed fragment composition with reference attribute grammars. In: Grassi, V., Mirandola, R., Medvidovic, N., Larsson, M. (eds.) Proceedings of the 15th ACM SIGSOFT Symposium on Component Based Software Engineering, CBSE 2012, Part of Comparch 12 Federated Events on Component-Based Software Engineering and Software Architecture, pp. 109–114. ACM (2012)

113.

Kitchenham, B.: Procedures for Performing Systematic Reviews (2004)

114.

Kounev, S., Brosig, F., Huber, N.: Self-aware QoS management in virtualized infrastructures. In: Proceedings of the 8th ACM International Conference on Autonomic Computing, pp. 175–176. ACM (2011). https://doi.org/10.1145/1998582.1998615

115.

Kounev, S., Kephart, J.O., Milenkoski, A., Zhu, X. (eds.): Self-Aware Computing Systems. Springer, Cham (2017)

116.

Kounev, S., Lewis, P.R., Bellman, K.L., Bencomo, N., Cámara, J., Diaconescu, A., Esterle, L., Geihs, K., Giese, H., Götz, S., Inverardi, P., Kephart, J.O., Zisman, A.: The notion of self-aware computing. In: Self-Aware Computing Systems, pp. 3–16 (2017). https://doi.org/10.1007/978-3-319-47474-8_1

117.

Křikava, F., Collet, P., France, R.B.: Actress: domain-specific modeling of self-adaptive software architectures. In: Proceedings of the 29th Annual ACM Symposium on Applied Computing, SAC ’14, pp. 391–398. ACM, New York, NY, USA (2014). https://doi.org/10.1145/2554850.2555020

118.

Krikava, F., Rouvoy, R., Seinturier, L.: Infrastructure as runtime models: towards model-driven resource management. In: ACM/IEEE 18th International Conference on Model Driven Engineering Languages and Systems (MODELS), 2015, pp. 100–105 (2015). https://doi.org/10.1109/MODELS.2015.7338240

119.

Kuhn, A., Verwaest, T.: FAME—a polyglot library for metamodeling at runtime. In: Proceedings of the 3rd International Models@Runtime Workshop, pp. 57–66 (2008)

120.

Kusic, D., Kandasamy, N., Jiang, G.: Approximation modeling for the online performance management of distributed computing systems. In: 4th International Conference on Autonomic Computing, 2007. ICAC ’07, pp. 23–23 (2007). https://doi.org/10.1109/ICAC.2007.8

121.

Lee, J., Muthig, D., Naab, M.: An approach for developing service oriented product lines. In: Proceedings of the 12th International on Software Product Line Confer SPLC 2008, pp. 275–284 (2008). https://doi.org/10.1109/SPLC.2008.34

122.

Loulou, H., Saudrais, S., Soubra, H., Larouci, C.: Adapting security policy at runtime for connected autonomous vehicles. In: 2016 IEEE 25th International Conference on Enabling Technologies: Infrastructure for Collaborative Enterprises (WETICE), pp. 26–31 (2016). https://doi.org/10.1109/WETICE.2016.16

123.

Maes, P.: Concepts and experiments in computational reflection. In: Conference Proceedings on Object-Oriented Programming Systems, Languages and Applications, OOPSLA ’87, pp. 147–155. ACM, New York, NY, USA (1987). https://doi.org/10.1145/38765.38821

124.

Maier, M.W.: Architecting principles for systems-of-systems. Syst. Eng. 1(4), 267–284 (1998). https://doi.org/10.1002/(SICI)1520-6858(1998)1:4<267::AID-SYS3>3.0.CO;2-D

125.

Maoz, S.: Using model-based traces as runtime models. Computer 42(10), 0028–36 (2009)CrossRef

126.

Mocci, A., Sangiorgio, M.: Detecting component changes at run time with behavior models. Computing 95(3), 191–221 (2013). https://doi.org/10.1007/s00607-012-0214-z CrossRef

127.

Mongiello, M., Pelliccione, P., Sciancalepore, M.: Ac-contract: run-time verification of context-aware applications. In: Proceedings of the 10th International Symposium on Software Engineering for Adaptive and Self-Managing Systems, SEAMS ’15, pp. 24–34. IEEE Press, Piscataway, NJ, USA (2015). http://dl.acm.org/citation.cfm?id=2821357.2821363

128.

Morin, B., Fleurey, F., Bencomo, N., Jézéquel, J.M., Solberg, A., Dehlen, V., Blair, G.: An aspect-oriented and model-driven approach for managing dynamic variability. In: Model Driven Engineering Languages and Systems, pp. 782–796. Springer (2008)

129.

Morin, B., Nain, G., Barais, O., Jezequel, J.M.: Leveraging models from design-time to runtime. A live demo. In: Proceedings of the 4th Workshop on Models@run.time, pp. 21–30 (2009)

130.

Morin, B., Barais, O., Jezequel, J., Fleurey, F., Solberg, A.: Models@ run. time to support dynamic adaptation. Computer 42(10), 44–51 (2009)CrossRef

131.

Mosincat, A.D., Binder, W.: Self-tuning BPEL processes. In: Proceedings of the 6th International Conference on Autonomic Computing, pp. 47–48. ACM (2009). https://doi.org/10.1145/1555228.1555239

132.

Moyano, F., Fernandez-Gago, C., Lopez, J.: A model-driven approach for engineering trust and reputation into software services. J. Netw. Comput. Appl. 69, 134–151 (2016). https://doi.org/10.1016/j.jnca.2016.04.018. http://www.sciencedirect.com/science/article/pii/S1084804516300698

133.

Mullins, R.: The EternalS Roadmap—Defining a Research Agenda for Eternal Systems, pp. 135–147. Springer, Berlin (2013). https://doi.org/10.1007/978-3-642-45260-4_10

134.

Nascimento, A., Rubira, C., Castor, F.: Using CVL to support self-adaptation of fault-tolerant service compositions. In: IEEE 7th International Conference on Self-Adaptive and Self-Organizing Systems (SASO), 2013, pp. 261–262 (2013). https://doi.org/10.1109/SASO.2013.34

135.

Neamtiu, I.G.: Practical Dynamic Software Updating. Ph.D. Thesis (2008)

136.

Park, S., Hinchey, M., In, H.P., Schmid, K.: 8th International workshop on dynamic software product lines (dspl 2014). In: Proceedings of the 18th International Software Product Line Conference—Volume 1, SPLC ’14, pp. 355–355. ACM, New York, NY, USA (2014). https://doi.org/10.1145/2648511.2648554

137.

Parra, C., Blanc, X., Cleve, A., Duchien, L.: Unifying design and runtime software adaptation using aspect models. Sci. Comput. Program. 76(12), 1247–1260 (2011). https://doi.org/10.1016/j.scico.2010.12.005 CrossRef

138.

Pasquale, L., Baresi, L., Nuseibeh, B.: Towards adaptive systems through requirements@runtime. In: Proceedings of the 6th Workshop on Models@run.time, pp. 13–24 (2011)

139.

Paucar, L.H.G., Bencomo, N., Yuen, K.K.F.: Juggling preferences in a world of uncertainty. In: 25th IEEE International Requirements Engineering Conference, RE 2017, Lisbon, Portugal, 4–8 September 2017, pp. 430–435 (2017). https://doi.org/10.1109/RE.2017.12

140.

Paucar, L.H.G., Bencomo, N.: Runtime models based on dynamic decision networks: enhancing the decision-making in the domain of ambient assisted living applications. In: Proceedings of the 11th International Workshop on Models@run.time Co-located with 19th International Conference on Model Driven Engineering Languages and Systems (MODELS 2016), Saint Malo, France, 4 October 2016, pp. 9–17 (2016). http://ceur-ws.org/Vol-1742/MRT16_paper_12.pdf

141.

Pickering, B., Robert, S., Menoret, S., Mengusoglu, E.: Model-driven management of complex systems. In: Proceedings of the 3rd International Models@ Runtime Workshop, pp. 117–126 (2008)

142.

Piechnick, C., Piechnick, M., Götz, S., Püschel, G., Aßmann, U.: Managing distributed context models requires adaptivity too. In: Götz, S., Bencomo, N., Blair, G., Song, H. (eds.) Proceedings of the 10th International Workshop on Models@run.time, pp. 61–70. CEUR-WS.org (2015)

143.

Porter, J., Menascé, D.A., Gomaa, H.: Desarm: a decentralized mechanism for discovering software architecture models at runtime in distributed systems. In: Proceedings of the 11th International Workshop on Models@run.time Co-located with 19th International Conference on Model Driven Engineering Languages and Systems (MODELS 2016), Saint Malo, France, 4 October 2016, pp. 43–51 (2016). http://ceur-ws.org/Vol-1742/MRT16_paper_3.pdf

144.

Ramirez, A.J., Cheng, B.H., Bencomo, N., Sawyer, P.: Relaxing claims: coping with uncertainty while evaluating assumptions at run time. In: Proceedings of the 15th International Conference on Model Driven Engineering Languages and Systems, pp. 53–69. Springer (2012)

145.

Ramirez, A.J., Jensen, A.C., Cheng, B.H.C.: A taxonomy of uncertainty for dynamically adaptive systems. In: Proceedings of the 7th International Symposium on Software Engineering for Adaptive and Self-Managing Systems, SEAMS ’12, pp. 99–108. IEEE Press, Piscataway, NJ, USA (2012). http://dl.acm.org/citation.cfm?id=2666795.2666812

146.

Redlich, D., Blair, G.S., Rashid, A., Molka, T., Gilani, W.: Research challenges for business process models at run-time. In: Models@run.time—Foundations, Applications, and Roadmaps (Dagstuhl Seminar 11481, 27 November–2 December 2011), pp. 208–236 (2014). https://doi.org/10.1007/978-3-319-08915-7_8

147.

Ressia, J., Renggli, L., Girba, T., Nierstrasz, O.: Run-time evolution through explicit meta-objects. In: Proceedings of the 5th Workshop on Models@run.time, pp. 37–48 (2010)

148.

Riva, C., Rodriguez, J.V.: Combining static and dynamic views for architecture reconstruction. In: Proceedings of the 6th European Conference on Software Maintenance and Reengineering, pp. 47–55 (2002). https://doi.org/10.1109/CSMR.2002.995789

149.

Rothenberg, J., Widman, L.E., Loparo, K.A., Nielsen, N.R.: The nature of modeling. In: Artificial Intelligence, Simulation and Modeling, pp. 75–92. Wiley (1989)

150.

Sabatucci, L., Cossentino, M.: From means-end analysis to proactive means-end reasoning. In: Proceedings of the 10th International Symposium on Software Engineering for Adaptive and Self-Managing Systems, SEAMS ’15, pp. 2–12. IEEE Press, Piscataway, NJ, USA (2015). http://dl.acm.org/citation.cfm?id=2821357.2821361

151.

Salehie, M., Tahvildari, L.: Self-adaptive software: landscape and research challenges. ACM Trans. Auton. Adapt. Syst. 4(2), 141–1442 (2009). https://doi.org/10.1145/1516533.1516538 CrossRef

152.

Samuel, A.L.: Some studies in machine learning using the game of checkers. IBM J. Res. Dev. 44(1.2), 206–226 (2000). https://doi.org/10.1147/rd.441.0206 CrossRef

153.

Sanchez, M., Barrero, I., Villalobos, J., Deridder, D.: An execution platform for extensible runtime models. In: Proceedings of the 3rd International Models@ Runtime Workshop, pp. 107–116 (2008)

154.

Saudrais, S., Staikopoulos, A., Clarke, S.: Using specification models for runtime adaptations. In: Proceedings of the 4th Workshop on Models@run.time, pp. 109–117 (2009)

155.

Sawyer, P., Bencomo, N., Whittle, J., Letier, E., Finkelstein, A.: Requirements-aware systems: a research agenda for re for self-adaptive systems. In: 2010 18th IEEE International Requirements Engineering Conference, pp. 95–103 (2010). https://doi.org/10.1109/RE.2010.21

156.

Schneider, D., Becker, M., Trapp, M.: Approaching runtime trust assurance in open adaptive systems. In: Proceedings of the 6th International Symposium on Software Engineering for Adaptive and Self-Managing Systems, SEAMS ’11, pp. 196–201. ACM, New York, NY, USA (2011). https://doi.org/10.1145/1988008.1988036

157.

Schneider, D., Becker, M.: Runtime models for self-adaptation in the ambient assisted living domain. In: Proceedings of the 3rd International Models@ Runtime Workshop, pp. 47–56 (2008)

158.

Schneider, D., Trapp, M.: A safety engineering framework for open adaptive systems. In: 5th IEEE International Conference on Self-Adaptive and Self-Organizing Systems (SASO), 2011, pp. 89–98 (2011). https://doi.org/10.1109/SASO.2011.20

159.

Schneider, D., Trapp, M.: Conditional safety certification of open adaptive systems. ACM Trans. Auton. Adapt. Syst. 8(2), 8:1–8:20 (2013). https://doi.org/10.1145/2491465.2491467 CrossRef

160.

Schöne, R., Götz, S., Aßmann, U., Bürger, C.: Incremental runtime-generation of optimisation problems using rag-controlled rewriting. In: Proceedings of the 11th International Workshop on Models@run.time Co-located with 19th International Conference on Model Driven Engineering Languages and Systems (MODELS 2016), Saint Malo, France, 4 October 2016, pp. 26–34 (2016). http://ceur-ws.org/Vol-1742/MRT16_paper_5.pdf

161.

Sheikh, M.B., Minhas, U.F., Khan, O.Z., Aboulnaga, A., Poupart, P., Taylor, D.J.: A Bayesian approach to online performance modeling for database appliances using Gaussian models. In: Proceedings of the 8th ACM International Conference on Autonomic Computing, pp. 121–130. ACM (2011). https://doi.org/10.1145/1998582.1998603

162.

Simmonds, J., Ben-David, S., Chechik, M.: Monitoring and recovery for web service applications. Computing 95(3), 223–267 (2013). https://doi.org/10.1007/s00607-012-0215-y CrossRefMATH

163.

Song, H., Huang, G., Chauvel, F., Sun, Y.: Applying MDE tools at runtime: experiments upon runtime models. In: Proceedings of the 5th Workshop on Models@run.time, pp. 25–36 (2010). (Tool demo paper)

164.

Song, H., Huang, G., Xiong, Y.F., Chauvel, F., Sun, Y., Mei, H., et al.: Inferring meta-models for runtime system data from the clients of management APIs. In: Proceedings of the 13th International Conference on Model-Driven Engineering Languages and Systems (MODELS 2010), vol. 6395 (2010)

165.

Song, H., Xiong, Y., Chauvel, F., Huang, G., Hu, Z., Mei, H.: Generating synchronization engines between running systems and their model-based views. In: Proceedings of the 4th Workshop on Models@run.time, pp. 11–20 (2009)

166.

Song, H., Zhang, X., Ferry, N., Chauvel, F., Solberg, A., Huang, G.: Modelling adaptation policies as domain-specific constraints. In: Model-Driven Engineering Languages and Systems, pp. 269–285. Springer (2014)

167.

Spinner, S., Kounev, S., Zhu, X., Lu, L., Uysal, M., Holler, A., Griffith, R.: Runtime vertical scaling of virtualized applications via online model estimation. In: IEEE 8th International Conference on Self-Adaptive and Self-Organizing Systems (SASO), 2014, pp. 157–166 (2014). https://doi.org/10.1109/SASO.2014.29

168.

Staikopoulos, A., Saudrais, S., Clarke, S., Padget, J., Cliffe, O., De Vos, M.: Mutual dynamic adaptation of models and service enactment in alive. In: Proceedings of the 3rd International Models@ Runtime Workshop, pp. 26–35 (2008)

169.

Stehle, E., Lynch, K., Shevertalov, M., Rorres, C., Mancoridis, S.: On the use of computational geometry to detect software faults at runtime. In: Proceedings of the 7th International Conference on Autonomic Computing, pp. 109–118. ACM (2010). https://doi.org/10.1145/1809049.1809069

170.

Szvetits, M., Zdun, U.: Enhancing root cause analysis with runtime models and interactive visualizations. In: Proceedings of the 8th Workshop on Models@run.time, pp. 38–49. CEUR-WS.org (2013)

171.

Szvetits, M., Zdun, U.: Reusable event types for models at runtime to support the examination of runtime phenomena. In: ACM/IEEE 18th International Conference on Model Driven Engineering Languages and Systems (MODELS), 2015, pp. 4–13 (2015). https://doi.org/10.1109/MODELS.2015.7338230

172.

Szvetits, M., Zdun, U.: Systematic literature review of the objectives, techniques, kinds, and architectures of models at runtime. Softw. Syst. Model. 15(1), 31–69 (2016)CrossRef

173.

Tallabaci, G., Souza, V.E.S.: Engineering adaptation with Zanshin: an experience report. In: Proceedings of the 8th International Symposium on Software Engineering for Adaptive and Self-Managing Systems, SEAMS ’13, pp. 93–102. IEEE Press, Piscataway, NJ, USA (2013)

174.

Tamura, G., Villegas, N.M., Müller, H.A., Duchien, L., Seinturier, L.: Improving context-awareness in self-adaptation using the dynamico reference model. In: Proceedings of the 8th International Symposium on Software Engineering for Adaptive and Self-Managing Systems, SEAMS ’13, pp. 153–162. IEEE Press, Piscataway, NJ, USA (2013)

175.

Tanvir Al Amin, M., Li, S., Rahman, M., Seetharamu, P., Wang, S., Abdelzaher, T., Gupta, I., Srivatsa, M., Ganti, R., Ahmed, R., Le, H.: Social trove: a self-summarizing storage service for social sensing. In: IEEE International Conference on Autonomic Computing (ICAC), 2015, pp. 41–50 (2015). https://doi.org/10.1109/ICAC.2015.47

176.

Taylor, R.N., Medvidovic, N., Oreizy, P.: Architectural styles for runtime software adaptation. In: Joint Working IEEE/IFIP Conference on Software Architecture, 2009 and European Conference on Software Architecture. WICSA/ECSA 2009, pp. 171–180. IEEE (2009). (Need to define for fundamental)

177.

Vasconcelos, A., Werner, C.: Software architecture recovery based on dynamic analysis. In: XVIII Brazilian Symposium on Software Engineering, Workshop on Modern Software Maintenance (2004)

178.

Vialon, A., Tei, K., Aknine, S.: Soft-goal approximation context awareness of goal-driven self-adaptive systems. In: 2017 IEEE International Conference on Autonomic Computing (ICAC), pp. 233–238 (2017). https://doi.org/10.1109/ICAC.2017.25

179.

Vogel, T., Giese, H.: A language for feedback loops in self-adaptive systems: executable runtime megamodels. In: Proceedings of the 6th International Symposium on Software Engineering for Adaptive and Self-Managing Systems, pp. 129–138 (2012). https://doi.org/10.1109/SEAMS.2012.6224399

180.

Vogel, T., Giese, H.: Language and framework requirements for adaptation models. In: Proceedings of the 6th Workshop on Models@run.time, pp. 1–12 (2011)

181.

Vogel, T., Giese, H.: On unifying development models and runtime models. In: Götz, S., Bencomo, N., France R. (eds.) Proceedings of the 9th International Workshop on Models@run.time, pp. 5–10. CEUR-WS.org (2014)

182.

Vogel, T., Seibel, A., Giese, H.: Toward megamodels at runtime. In: Proceedings of the 5th Workshop on Models@run.time, pp. 13–24 (2010)

183.

Vogel, T., Giese, H.: Model-driven engineering of self-adaptive software with eurema. ACM Trans. Auton. Adapt. Syst. 8(4), 18:1–18:33 (2014). https://doi.org/10.1145/2555612 CrossRef

184.

Vrbaski, M., Mussbacher, G., Petriu, D., Amyot, D.: Goal models as run-time entities in context-aware systems. In: Proceedings of the 7th Workshop on Models@Run.Time, MRT ’12, pp. 3–8. ACM, New York, NY, USA (2012). https://doi.org/10.1145/2422518.2422520

185.

Walter, J., Marco, A.D., Spinner, S., Inverardi, P., Kounev, S.: Online learning of run-time models for performance and resource management in data centers. In: Self-Aware Computing Systems, pp. 507–528. IEEE Press, Los Alamitos, CA, USA (2017). https://doi.org/10.1007/978-3-319-47474-8_17

186.

Wätzold, S., Giese, H.: Classifying distributed self-* systems based on runtime models and their coupling. In: Götz, S., Bencomo, N., France, R. (eds.) Proceedings of the 9th International Workshop on Models@run.time, pp. 11–20. CEUR-WS.org (2014)

187.

Weissbach, M., Chrszon, P., Springer, T., Schill, A.: Decentralized coordination of adaptations in distributed self-adaptive software systems. In: 2017 IEEE 11th International Conference on Self-Adaptive and Self-Organizing Systems (SASO) (2017)

188.

Welsh, K., Bencomo, N., Sawyer, P., Whittle, J.: Self-explanation in adaptive systems based on runtime goal-based models, pp. 122–145 (2014). https://doi.org/10.1007/978-3-662-44871-7_5

189.

Welsh, K., Sawyer, P., Bencomo, N.: Run-time resolution of uncertainty. In: RE 2011, 19th IEEE International Requirements Engineering Conference, Trento, Italy, 29 August 2011–2 September 2011, pp. 355–356 (2011). https://doi.org/10.1109/RE.2011.6051673

190.

Weyns, D., Iftikhar, M.U., Söderlund, J.: Do external feedback loops improve the design of self-adaptive systems? A controlled experiment. In: Proceedings of the 8th International Symposium on Software Engineering for Adaptive and Self-Managing Systems, SEAMS ’13, pp. 3–12. IEEE Press, Piscataway, NJ, USA (2013). http://dl.acm.org/citation.cfm?id=2487336.2487341

191.

Wolfe, C., Graham, T.N., Phillips, W.G.: An incremental algorithm for high-performance runtime model consistency. In: Model Driven Engineering Languages and Systems, pp. 357–371. Springer (2009)

192.

Zhang, X., Chen, X., Zhang, Y., Wu, Y., Yao, W., Huang, G., Lin, Q.: Runtime model based management of diverse cloud resources. In: Model-Driven Engineering Languages and Systems, pp. 572–588. Springer (2013)

193.

Zhong, C., DeLoach, S.A.: Runtime models for automatic reorganization of multi-robot systems. In: Proceedings of the 6th International Symposium on Software Engineering for Adaptive and Self-Managing Systems, SEAMS ’11, pp. 20–29. ACM, New York, NY, USA (2011). https://doi.org/10.1145/1988008.1988012

Titel: Models@run.time: a guided tour of the state of the art and research challenges
verfasst von: Nelly Bencomo
Sebastian Götz
Hui Song
Publikationsdatum: 09.01.2019
Verlag: Springer Berlin Heidelberg
Erschienen in: Software and Systems Modeling / Ausgabe 5/2019
Print ISSN: 1619-1366
Elektronische ISSN: 1619-1374
DOI: https://doi.org/10.1007/s10270-018-00712-x

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