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

Published:21 June 2021Publication 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).Google ScholarGoogle Scholar
  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/doi:10.1145/3387940.3392089 Invited Keynote.Google ScholarGoogle ScholarDigital LibraryDigital Library
  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.Google ScholarGoogle ScholarDigital LibraryDigital Library
  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.Google ScholarGoogle ScholarDigital LibraryDigital Library
  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.Google ScholarGoogle ScholarCross RefCross Ref
  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.Google ScholarGoogle ScholarDigital LibraryDigital Library
  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.2019.2953499Google ScholarGoogle ScholarCross RefCross Ref
  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.Google ScholarGoogle ScholarCross RefCross Ref
  9. Antonia Bertolino. 2007. Software testing research: Achievements, challenges, dreams. In Future of Software Engineering (FOSE’07). IEEE, 85–103.Google ScholarGoogle Scholar
  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.Google ScholarGoogle ScholarCross RefCross Ref
  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.2018.00031Google ScholarGoogle ScholarCross RefCross Ref
  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.2007.913919Google ScholarGoogle ScholarDigital LibraryDigital Library
  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.Google ScholarGoogle Scholar
  14. Koen Claessen and John Hughes. 2002. Testing monadic code with QuickCheck. ACM SIGPLAN Notices 37, 12 (2002), 47–59.Google ScholarGoogle ScholarDigital LibraryDigital Library
  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.2017.09.015Google ScholarGoogle ScholarCross RefCross Ref
  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/10.1145/138859.138867Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. Claire Le Goues, Michael Pradel, and Abhik Roychoudhury. 2019. Automated program repair. Commun. ACM 62, 12 (2019), 56–65.Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. Michael Grieves. 2015. Digital Twin: Manufacturing Excellence through Virtual Factory Replication. (2015).Google ScholarGoogle Scholar
  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_4Google ScholarGoogle Scholar
  20. David Ha and Jürgen Schmidhuber. 2018. World Models. CoRR abs/1803.10122(2018). arxiv:1803.10122http://arxiv.org/abs/1803.10122Google ScholarGoogle Scholar
  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/1912.01603Google ScholarGoogle Scholar
  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/Google ScholarGoogle Scholar
  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.Google ScholarGoogle Scholar
  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.Google ScholarGoogle ScholarDigital LibraryDigital Library
  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.Google ScholarGoogle ScholarDigital LibraryDigital Library
  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/10.1145/2593929.2600116Google ScholarGoogle ScholarDigital LibraryDigital Library
  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.Google ScholarGoogle Scholar
  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.Google ScholarGoogle Scholar
  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.Google ScholarGoogle ScholarDigital LibraryDigital Library
  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.Google ScholarGoogle ScholarCross RefCross Ref
  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.Google ScholarGoogle Scholar
  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.Google ScholarGoogle Scholar
  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.Google ScholarGoogle ScholarDigital LibraryDigital Library
  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.htmlGoogle ScholarGoogle Scholar
  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.Google ScholarGoogle Scholar
  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/Google ScholarGoogle Scholar
  37. Bryan Lim and Mihaela van der Schaar. 2018. Disease-Atlas: Navigating Disease Trajectories with Deep Learning. arxiv:1803.10254 [stat.ML]Google ScholarGoogle Scholar
  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.Google ScholarGoogle ScholarDigital LibraryDigital Library
  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/1301.3781Google ScholarGoogle Scholar
  40. Silvia Milano, Mariarosaria Taddeo, and Luciano Floridi. 2021. Recommender systems and their ethical challenges. AI and Society 35(2021), 957 – 967.Google ScholarGoogle ScholarDigital LibraryDigital Library
  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.Google ScholarGoogle Scholar
  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.Google ScholarGoogle Scholar
  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/doi:10.1109/TEVC.2017.2693219Google ScholarGoogle ScholarCross RefCross Ref
  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.Google ScholarGoogle Scholar
  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.Google ScholarGoogle Scholar
  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.Google ScholarGoogle ScholarCross RefCross Ref
  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/1911.08265Google ScholarGoogle Scholar
  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.pdfGoogle ScholarGoogle ScholarDigital LibraryDigital Library
  49. Mark Slee, Aditya Agarwal, and Marc Kwiatkowski. 2007. Thrift:Scalable cross-language services implementation. Facebook white paper 5, 8 (2007), 127.Google ScholarGoogle Scholar
  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.2017.72Google ScholarGoogle ScholarDigital LibraryDigital Library
  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.Google ScholarGoogle Scholar
  52. Sergio Terzi and Sergio Cavalieri. 2004. Simulation in the supply chain context: a survey. Computers in Industry 53, 1 (2004), 3–16.Google ScholarGoogle ScholarDigital LibraryDigital Library
  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]Google ScholarGoogle Scholar
  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.Google ScholarGoogle ScholarDigital LibraryDigital Library
  55. Shiwen Wu, Wentao Zhang, Fei Sun, and Bin Cui. 2020. Graph Neural Networks in Recommender Systems: A Survey. arxiv:2011.02260 [cs.IR]Google ScholarGoogle Scholar
  56. Geogios N Yannakakis. 2012. Game AI revisited. In Proceedings of the 9th conference on Computing Frontiers. 285–292.Google ScholarGoogle ScholarDigital LibraryDigital Library
  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]Google ScholarGoogle Scholar
  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.Google ScholarGoogle ScholarDigital LibraryDigital Library
  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.htmlGoogle ScholarGoogle Scholar
  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.Google ScholarGoogle ScholarDigital LibraryDigital Library
  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.Google ScholarGoogle Scholar

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  • Published in

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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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    • Published: 21 June 2021

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