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Facebook’s Cyber–Cyber and Cyber–Physical Digital Twins

Published: 21 June 2021 Publication History

Abstract

A cyber–cyber digital twin is a simulation of a software system. By contrast, a cyber–physical digital twin is a simulation of a non-software (physical) system. Although cyber–physical digital twins have received a lot of recent attention, their cyber–cyber counterparts have been comparatively overlooked. In this paper we show how the unique properties of cyber–cyber digital twins open up exciting opportunities for research and development. Like all digital twins, the cyber–cyber digital twin is both informed by and informs the behaviour of the twin it simulates. It is therefore a software system that simulates another software system, making it conceptually truly a twin, blurring the distinction between the simulated and the simulator. Cyber–cyber digital twins can be twins of other cyber–cyber digital twins, leading to a hierarchy of twins. As we shall see, these apparently philosophical observations have practical ramifications for the design, implementation and deployment of digital twins at Facebook.

References

[1]
David Adam. 2020. Special report: The simulations driving the world’s response to COVID-19. Nature (April 2020).
[2]
John Ahlgren, Maria Eugenia Berezin, Kinga Bojarczuk, Elena Dulskyte, Inna Dvortsova, Johann George, Natalija Gucevska, Mark Harman, Ralf Laemmel, Erik Meijer, Silvia Sapora, and Justin Spahr-Summers. 2020. WES: Agent-based User Interaction Simulation on Real Infrastructure. In GI @ ICSE 2020, Shin Yoo, Justyna Petke, Westley Weimer, and Bobby R. Bruce (Eds.). ACM, 276–284. https://doi.org/ Invited Keynote.
[3]
John Ahlgren, Maria Eugenia Berezin, Kinga Bojarczuk, Elena Dulskyte, Inna Dvortsova, Johann George, Natalija Gucevska, Mark Harman, Maria Lomeli, Erik Meijer, Silvia Sapora, and Justin Spahr-Summers. 2021. Testing Web Enabled Simulation at Scale Using Metamorphic Testing. In International Conference on Software Engineering (ICSE) Software Engineering in Practice (SEIP) track. Virtual.
[4]
Saif Al-Sultan, Moath M. Al-Doori, Ali H. Al-Bayatti, and Hussien Zedan. 2014. A comprehensive survey on vehicular Ad Hoc network. Journal of Network and Computer Applications 37 (2014), 380 – 392.
[5]
Nadia Alshahwan, Xinbo Gao, Mark Harman, Yue Jia, Ke Mao, Alexander Mols, Taijin Tei, and Ilya Zorin. 2018. Deploying Search Based Software Engineering with Sapienz at Facebook (keynote paper). In 10th International Symposium on Search Based Software Engineering (SSBSE 2018). Montpellier, France, 3–45. Springer LNCS 11036.
[6]
Saswat Anand, Antonia Bertolino, Edmund Burke, Tsong Yueh Chen, John Clark, Myra B. Cohen, Wolfgang Grieskamp, Mark Harman, Mary Jean Harrold, Jenny Li, Phil McMinn, and Hong Zhu. 2013. An orchestrated survey of methodologies for automated software test case generation. Journal of Systems and Software 86, 8 (August 2013), 1978–2001.
[7]
B. R. Barricelli, E. Casiraghi, and D. Fogli. 2019. A Survey on Digital Twin: Definitions, Characteristics, Applications, and Design Implications. IEEE Access 7(2019), 167653–167671. https://doi.org/10.1109/ACCESS.2019.2953499
[8]
Peter Bauer, Bjorn Stevens, and Wilco Hazeleger. 2021. A digital twin of Earth for the green transition. Nature Climate Change 11(2021), 80 – 83.
[9]
Antonia Bertolino. 2007. Software testing research: Achievements, challenges, dreams. In Future of Software Engineering (FOSE’07). IEEE, 85–103.
[10]
Saul Blecker, Stuart Katz, LI Horwitz, Gilad Kuperman, H Park, A Gold, and David Sontag. 2016. Comparison of approaches for heart failure case identification from electronic health record data. JAMA Cardiology 1, 9 (2016), 1014–1020.
[11]
Koen Bruynseels, Filippo Santoni de Sio, and Jeroen van den Hoven. 2018. Digital Twins in Health Care: Ethical Implications of an Emerging Engineering Paradigm. Frontiers in Genetics 9(2018), 31. https://doi.org/10.3389/fgene.2018.00031
[12]
L. Busoniu, R. Babuska, and B. De Schutter. 2008. A Comprehensive Survey of Multiagent Reinforcement Learning. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews) 38, 2 (2008), 156–172. https://doi.org/10.1109/TSMCC.2007.913919
[13]
C. Calcagno, D. Distefano, J. Dubreil, D. Gabi, P. Hooimeijer, M. Luca, P. W. O’Hearn, I. Papakonstantinou, J. Purbrick, and D. Rodriguez. 2015. Moving Fast with Software Verification. In NASA Formal Methods - 7th International Symposium. 3–11.
[14]
Koen Claessen and John Hughes. 2002. Testing monadic code with QuickCheck. ACM SIGPLAN Notices 37, 12 (2002), 47–59.
[15]
Magdalini Eirinaki, Jerry Gao, Iraklis Varlamis, and Konstantinos Tserpes. 2018. Recommender Systems for Large-Scale Social Networks: A review of challenges and solutions. Future Generation Computer Systems 78 (2018), 413–418. https://doi.org/10.1016/j.future.2017.09.015
[16]
David Goldberg, David Nichols, Brian M. Oki, and Douglas Terry. 1992. Using Collaborative Filtering to Weave an Information Tapestry. Commun. ACM 35, 12 (Dec. 1992), 61–70. https://doi.org/10.1145/138859.138867
[17]
Claire Le Goues, Michael Pradel, and Abhik Roychoudhury. 2019. Automated program repair. Commun. ACM 62, 12 (2019), 56–65.
[18]
Michael Grieves. 2015. Digital Twin: Manufacturing Excellence through Virtual Factory Replication. (2015).
[19]
Michael Grieves and John Vickers. 2017. Digital Twin: Mitigating Unpredictable, Undesirable Emergent Behavior in Complex Systems. In Transdisciplinary Perspectives on Complex Systems: New Findings and Approaches, Franz-Josef Kahlen, Shannon Flumerfelt, and Anabela Alves(Eds.). Springer International Publishing, 85–113. https://doi.org/10.1007/978-3-319-38756-7_4
[20]
David Ha and Jürgen Schmidhuber. 2018. World Models. CoRR abs/1803.10122(2018). arxiv:1803.10122http://arxiv.org/abs/1803.10122
[21]
Danijar Hafner, Timothy P. Lillicrap, Jimmy Ba, and Mohammad Norouzi. 2019. Dream to Control: Learning Behaviors by Latent Imagination. CoRR abs/1912.01603(2019). arxiv:1912.01603http://arxiv.org/abs/1912.01603
[22]
Thurow K Haghi M and Stoll R.2017. Wearable Devices in Medical Internet of Things: Scientific Research and Commercially Available Devices.Healthc Inform Res. 1(2017). https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5334130/
[23]
Yoni Halpern, Steven Horng, and David Sontag. 2016. Clinical Tagging with Joint Probabilistic Models. In Proceedings of the 1st Machine Learning for Healthcare Conference(Proceedings of Machine Learning Research, Vol. 56), Finale Doshi-Velez, Jim Fackler, David Kale, Byron Wallace, and Jenna Wiens (Eds.). 209–225.
[24]
Mark Harman. 2007. The current state and future of Search Based Software Engineering. In Future of Software Engineering 2007, Lionel Briand and Alexander Wolf (Eds.). IEEE Computer Society Press, Los Alamitos, California, USA. This volume.
[25]
Mark Harman, Yue Jia, Jens Krinke, Bill Langdon, Justyna Petke, and Yuanyuan Zhang. 2014. Search based software engineering for software product line engineering: a survey and directions for future work (Keynote Paper). In 18th International Software Product Line Conference (SPLC 14). Florence, Italy, 5–18.
[26]
Mark Harman, Yue Jia, William B. Langdon, Justyna Petke, Iman Hemati Moghadam, Shin Yoo, and Fan Wu. 2014. Genetic Improvement for Adaptive Software Engineering (Keynote Paper). In 9th International Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS 2014) (Hyderabad, India). ACM, New York, NY, USA, 1–4. https://doi.org/10.1145/2593929.2600116
[27]
Mark Harman, Phil McMinn, Jerffeson Teixeira de Souza, and Shin Yoo. 2012. Search Based Software Engineering: Techniques, Taxonomy, Tutorial. In Empirical software engineering and verification: LASER 2009-2010, Bertrand Meyer and Martin Nordio (Eds.). Springer, 1–59. LNCS 7007.
[28]
Eugene Ie, Chih-wei Hsu, Martin Mladenov, Vihan Jain, Sanmit Narvekar, Jing Wang, Rui Wu, and Craig Boutilier. 2019. RecSim: A Configurable Simulation Platform for Recommender Systems. arXiv e-prints (Sep 2019), arXiv:1909.04847.
[29]
Yue Jia and Mark Harman. 2011. An Analysis and Survey of the Development of Mutation Testing. IEEE Transactions on Software Engineering 37, 5 (September–October 2011), 649 – 678.
[30]
Gregory L Johnson, Clayton L Hanson, Stuart P Hardegree, and Edward B Ballard. 1996. Stochastic weather simulation: Overview and analysis of two commonly used models. Journal of Applied Meteorology 35, 10 (1996), 1878–1896.
[31]
Sabrine Kalboussi, Slim Bechikh, Marouane Kessentini, and Lamjed Ben Said. 2013. On the Influence of the Number of Objectives in Evolutionary Autonomous Software Agent Testing. In 25th International Conference on Tools with Artificial Intelligence (ICTAI ’13). IEEE, Herndon, VA, USA, 229–234.
[32]
Jack PC Kleijnen. 2005. Supply chain simulation tools and techniques: a survey. International journal of simulation and process modelling 1, 1-2(2005), 82–89.
[33]
Christian Krupitzer, Felix Maximilian Roth, Sebastian Van Syckel, Gregor Schiele, and Christian Becker. 2015. A survey on engineering approaches for self-adaptive systems. Pervasive Mobile Computing 17 (2015), 184–206.
[34]
Trent Kyono, Fiona J. Gilbert, and Mihaela van der Schaar. 2019. Multi-view Multi-task Learning for Improving Autonomous Mammogram Diagnosis. In Proceedings of the 4th Machine Learning for Healthcare Conference. PMLR, 571–591. http://proceedings.mlr.press/v106/kyono19a.html
[35]
Benjamin Letham and Eytan Bakshy. 2019. Bayesian Optimization for Policy Search via Online-Offline Experimentation. Journal of Machine Learning Research 20 (2019), 145:1–145:30.
[36]
Patricia Liceras. 2019. Singapore experiments with its digital twin to improve city life. https://www.smartcitylab.com/blog/digital-transformation/singapore-experiments-with-its-digital-twin-to-improve-city-life/
[37]
Bryan Lim and Mihaela van der Schaar. 2018. Disease-Atlas: Navigating Disease Trajectories with Deep Learning. arxiv:1803.10254 [stat.ML]
[38]
Alexandru Marginean, Johannes Bader, Satish Chandra, Mark Harman, Yue Jia, Ke Mao, Alexander Mols, and Andrew Scott. 2019. SapFix: Automated End-to-End Repair at Scale. In International Conference on Software Engineering (ICSE) Software Engineering in Practice (SEIP) track. Montreal, Canada.
[39]
Tomas Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean. 2013. Efficient Estimation of Word Representations in Vector Space. CoRR abs/1301.3781(2013). arxiv:1301.3781http://arxiv.org/abs/1301.3781
[40]
Silvia Milano, Mariarosaria Taddeo, and Luciano Floridi. 2021. Recommender systems and their ethical challenges. AI and Society 35(2021), 957 – 967.
[41]
Martin Mladenov, Chih-Wei Hsu, Vihan Jain, Eugene Ie, Christopher Colby, Nicolas Mayoraz, Hubert Pham, Dustin Tran, Ivan Vendrov, and Craig Boutilier. 2021. RecSim NG: Toward Principled Uncertainty Modeling for Recommender Ecosystems. arXiv:2103.08057 [cs] (March 2021). http://arxiv.org/abs/2103.08057 arXiv:2103.08057.
[42]
Cu Nguyen, Anna Perini, Paolo Tonella, Simon Miles, Mark Harman, and Michael Luck. 2009. Evolutionary Testing of Autonomous Software Agents. In 8th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2009). Budapest, Hungary, 521–528.
[43]
Justyna Petke, Saemundur O. Haraldsson, Mark Harman, William B. Langdon, David R. White, and John R. Woodward. 2018. Genetic Improvement of Software: a Comprehensive Survey. IEEE Transactions on Evolutionary Computation 22, 3 (June 2018), 415–432. https://doi.org/
[44]
A. Rasheed, O. San, and T. Kvamsdal. 2020. Digital Twin: Values, Challenges and Enablers From a Modeling Perspective. 8 (2020), 21980–22012. https://doi.org/10.1109/ACCESS.2020.2970143 Conference Name: IEEE Access.
[45]
David Rohde, Stephen Bonner, Travis Dunlop, Flavian Vasile, and Alexandros Karatzoglou. 2018. RecoGym: A Reinforcement Learning Environment for the problem of Product Recommendation in Online Advertising. arXiv:1808.00720 [cs] (Sept. 2018). http://arxiv.org/abs/1808.00720 arXiv:1808.00720.
[46]
Maya Rotmensch, Yoni Halpern, Abdulhakim Tlimat, Steven Horng, and David Sontag. 2017. Learning a Health Knowledge Graph from Electronic Medical Records. Nature Scientific Reports 7, 1 (2017), 5994.
[47]
Julian Schrittwieser, Ioannis Antonoglou, Thomas Hubert, Karen Simonyan, Laurent Sifre, Simon Schmitt, Arthur Guez, Edward Lockhart, Demis Hassabis, Thore Graepel, Timothy P. Lillicrap, and David Silver. 2019. Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model. CoRR abs/1911.08265(2019). arxiv:1911.08265http://arxiv.org/abs/1911.08265
[48]
Weiran Shen, Pingzhong Tang, and Song Zuo. 2019. Automated mechanism design via neural networks. In Proceedings of the 18th International Conference on Autonomous Agents and MultiAgent Systems. 215–223. https://arxiv.org/pdf/1805.03382.pdf
[49]
Mark Slee, Aditya Agarwal, and Marc Kwiatkowski. 2007. Thrift:Scalable cross-language services implementation. Facebook white paper 5, 8 (2007), 127.
[50]
B. Smith and G. Linden. 2017. Two Decades of Recommender Systems at Amazon.com. IEEE Internet Computing 21, 03 (may 2017), 12–18. https://doi.org/10.1109/MIC.2017.72
[51]
Margaret-Anne D. Storey and Alexey Zagalsky. 2016. Disrupting developer productivity one bot at a time. In Proceedings of the 24th International Symposium on Foundations of Software Engineering (FSE 2016), Seattle, WA, USA, November 13-18, 2016. ACM, 928–931.
[52]
Sergio Terzi and Sergio Cavalieri. 2004. Simulation in the supply chain context: a survey. Computers in Industry 53, 1 (2004), 3–16.
[53]
Wenjie Wang, Fuli Feng, Xiangnan He, Hanwang Zhang, and Tat-Seng Chua. 2020. ”Click” Is Not Equal to ”Like”: Counterfactual Recommendation for Mitigating Clickbait Issue. arxiv:2009.09945 [cs.IR]
[54]
Martin Ward. 1999. Assembler to C Migration using the FermaT Transformation System. In IEEE International Conference on Software Maintenance (ICSM’99) (Oxford, UK). IEEE Computer Society Press, Los Alamitos, California, USA.
[55]
Shiwen Wu, Wentao Zhang, Fei Sun, and Bin Cui. 2020. Graph Neural Networks in Recommender Systems: A Survey. arxiv:2011.02260 [cs.IR]
[56]
Geogios N Yannakakis. 2012. Game AI revisited. In Proceedings of the 9th conference on Computing Frontiers. 285–292.
[57]
Sirui Yao, Yoni Halpern, Nithum Thain, Xuezhi Wang, Kang Lee, Flavien Prost, Ed H. Chi, Jilin Chen, and Alex Beutel. 2021. Measuring Recommender System Effects with Simulated Users. arxiv:2101.04526 [cs.LG]
[58]
Shin Yoo and Mark Harman. 2012. Regression Testing Minimisation, Selection and Prioritisation: A Survey. Journal of Software Testing, Verification and Reliability 22, 2(2012), 67–120.
[59]
Jinsung Yoon, Ahmed Alaa, Scott Hu, and Mihaela Schaar. 2016. ForecastICU: A Prognostic Decision Support System for Timely Prediction of Intensive Care Unit Admission. In Proceedings of The 33rd International Conference on Machine Learning(Proceedings of Machine Learning Research, Vol. 48), Maria Florina Balcan and Kilian Q. Weinberger (Eds.). PMLR, 1680–1689. http://proceedings.mlr.press/v48/yoon16.html
[60]
Shuo Zhang and Krisztian Balog. 2020. Evaluating Conversational Recommender Systems via User Simulation. Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (Aug. 2020), 1512–1520. https://doi.org/10.1145/3394486.3403202 arXiv:2006.08732.
[61]
Yan Zheng, Changjie Fan, Xiaofei Xie, Ting Su, Lei Ma, Jianye Hao, Zhaopeng Meng, Yang Liu, Ruimin Shen, and Yingfeng Chen. 2019. Wuji: Automatic Online Combat Game Testing Using Evolutionary Deep Reinforcement Learning. In Automated software engineering (ASE). IEEE, 772–784.

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EASE '21: Proceedings of the 25th International Conference on Evaluation and Assessment in Software Engineering
June 2021
417 pages
ISBN:9781450390538
DOI:10.1145/3463274
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Publication History

Published: 21 June 2021

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  1. Digital Twins
  2. Web Enabled Simulation

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  • European Research Council (ERC)

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EASE 2021

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Overall Acceptance Rate 71 of 232 submissions, 31%

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  • (2024)SoK: A Holistic View of Cyberattacks Prediction with Digital Twins2024 Second International Conference on Emerging Trends in Information Technology and Engineering (ICETITE)10.1109/ic-ETITE58242.2024.10493514(1-7)Online publication date: 22-Feb-2024
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