Page 37 - 《软件学报》2021年第7期

檀超 等:复杂软件系统的不确定性 1955

[65] Kate S, Ore JP, Zhang XY, Elbaum S, Xu ZG. Phys: Probabilistic physical unit assignment and inconsistency detection. In: Proc. of
the 26th ACM Joint Meeting on European Software Engineering Conf. and Symp. on the Foundations of Software Engineering.
2018. 563–573.
[66] Ravi M, Demazeau Y, Ramparany F. Reasoning with trust and uncertainty illustration in the internet of things. In: Proc. of the 2015
IEEE/WIC/ACM Int’l Conf. on Web Intelligence and Intelligent Agent Technology (WI-IAT). 2015(2):125–128.
[67] Shin SY, Nejati S, Sabetzadeh M, Briand LC, Zimmer F. Test case prioritization for acceptance testing of cyberphysical systems: A
multi-objective search-based approach. In: Proc. of the 27th ACM SIGSOFT Int’l Symp. on Software Testingand Analysis. 2018.
49–60.
[68] Cimatti A, Micheli A, Roveri M. Timelines with temporal uncertainty. In: proc. of the 27th AAAI Conf. on Artificial Intelligence.
2013.
[69] Zhang M, Shaukat A, Yue T, et al. Uncertainty-wise cyber-physical system test modeling. Software & Systems Modeling, 2019,
18(2):1379–1418.
[70] Zhang M, Shaukat A, Yue T. Uncertainty-wise test case generation and minimization for cyber-physical systems. Journal of
Systems and Software, 2019,153:1–21.
[71] Ma T, Shaukat A, Yue T. Modeling foundations for executable model-based testing of self-healing cyber-physical systems.
Software & Systems Modeling, 2019,18(5):2843–2873.
[72] Potter K, Rosen P, Johnson CR. From quantification to visualization: A taxonomy of uncertainty visualization approaches. In: Proc.
of the IFIP Working Conf. on Uncertainty Quantification. Springer-Verlag, 2011. 226–249.
[73] De Rocquigny E, Devictor N, Tarantola S. Uncertainty in Industrial Practice: A Guide to Quantitative Uncertainty Management.
John Wiley & Sons, 2008.
[74] Mathias KE, Nixon MR, Talbot PJ. Uncertainty management in a decision-making system. USPatent, 2009,7:606,784.
[75] Kellermann K, Reynolds R. When ignorance is Bliss: The role of motivation to reduce uncertainty in uncertainty reduction theory.
Human Communication Research, 1990,17(1):5–75.
[76] Bertoa MF, Burgueño L, Moreno N, et al. Incorporating measurement uncertainty into OCL/UML primitive datatypes. Software &
Systems Modeling, 2019, 1–27.
[77] Aziz AR, et al. Decision theory with imperfect information. World Scientific, 2014(10).
[78] Akbar A, Kousiouris G, Pervaiz H, Sancho J, Ta-Shma P, Carrez F, Moessner K. Real-time probabilistic datafusion for large-scale
IoT applications. IEEE Access, 2018,6:10015–10027.
[79] Cámara J, Garlan D, Schmerl B. Synthesizing tradeoff spaces with quantitative guarantees for families of software systems. Journal
of Systems and Software, 2019,152:33–49.
[80] Chaves LC, Ismail HI, Bessa IV, Cordeiro LC, de Lima Filho EB. Verifying fragility in digital systems with uncertainties using
dsverifier v2.0. Journal of Systems and Software, 2019,153:22–43.
[81] Sharifloo A, Metzger A. MCaaS: Model checking in the cloud for assurances of adaptive systems. In: Software Engineering for
Self-adaptive Systems III. 2017. 137–153.
[82] Kasprzyk JR, Nataraj S, Reed PM, Lempert RJ. Many objective robust decision making for complex environment alsystems
undergoing change. Environmental Modelling & Software, 2013,42:55–71.
[83] Islam N, Azim A. Assuring the runtime behavior of self-adaptive cyber-physical systems using feature modeling. In: Proc. of the
28th Annual Int’l Conf. on Computer Science and Software Engineering. 2018. 48–59.
[84] Kläs M, Vollmer AM. Uncertainty in machine learning applications: A practice-driven classification of uncertainty. In: Proc. of the
Int’l Conf. on Computer Safety, Reliability, and Security. Springer-Verlag, 2018. 431–438.
[85] Gal Y, Ghahramani Z. Dropout as a bayesian approximation: Representing model uncertainty in deep learning. In: Proc. of the Int’l
Conf. on Machine Learning. 2016. 1050–1059.
[86] Lakshminarayanan B, Pritzel A, Blundell C. Simple and scalable predictive uncertainty estimation using deep ensembles. In:
Advances in Neural Information Processing Systems. 2017. 6402–6413.
[87] Nia MA, Kargahi M, Faghih F. Probabilistic approximation of runtime quantitative verification in self-adaptive systems.
Microprocessors and Microsystems, 2020,72:102943.