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李晓锋 等: 空间飞行器控制软件在轨自适应可信演化框架                                                     1287


                  [8]   Cheng BHC, Sawyer P, Bencomo N, Whittle J. A goal-based modeling approach to develop requirements of an adaptive system with
                     environmental uncertainty. In: Proc. of the 12th Int’l Conf. on Model Driven Engineering Languages and Systems. Denver: Springer,
                     2009. 468–483. [doi: 10.1007/978-3-642-04425-0_36]
                  [9]   Chu S, Koe J, Garlan D, Kang E. Integrating graceful degradation and recovery through requirement-driven adaptation. In: Proc. of the
                     19th IEEE/ACM Int’l Symp. on Software Engineering for Adaptive and Self-managing Systems. Lisbon: IEEE, 2024. 122–132. [doi: 10.
                     1145/3643915.3644090]
                 [10]   Aradea,  Supriana  I,  Surendro  K.  Self-adaptive  software  modeling  based  on  contextual  requirements.  TELKOMNIKA,  2018,  16(3):
                     1276–1288. [doi: 10.12928/TELKOMNIKA.v16i3.7032]
                 [11]   Moreno GA, Cámara J, Garlan D, Schmerl B. Proactive self-adaptation under uncertainty: A probabilistic model checking approach. In:
                     Proc. of the 10th Joint Meeting on Foundations of Software Engineering. Bergamo: ACM, 2015. 1–12. [doi: 10.1145/2786805.2786853]
                 [12]   Cetina C, Giner P, Fons J, Pelechano V. Autonomic computing through reuse of variability models at runtime: The case of smart homes.
                     Computer, 2009, 42(10): 37–43. [doi: 10.1109/MC.2009.309]
                 [13]   Salehie M, Pasquale L, Omoronyia I, Ali R, Nuseibeh B. Requirements-driven adaptive security: Protecting variable assets at runtime. In:
                     Proc. of the 20th IEEE Int’l Requirements Engineering Conf. Chicago: IEEE, 2012. 111–120. [doi: 10.1109/RE.2012.6345794]
                 [14]   Filho  RR,  Alberts  E,  Gerostathopoulos  I,  Porter  B,  Costa  FM.  Emergent  Web  server:  An  exemplar  to  explore  online  learning  in
                     compositional self-adaptive systems. In: Proc. of the 2022 Int’l Symp. on Software Engineering for Adaptive and Self-managing Systems.
                     Pittsburgh: IEEE, 2022. 36–42. [doi: 10.1145/3524844.3528079]
                 [15]   Lee E, Kim YG, Seo YD, Seol K, Baik DK. RINGA: Design and verification of finite state machine for self-adaptive software at runtime.
                     Information and Software Technology, 2018, 93: 200–222. [doi: 10.1016/j.infsof.2017.09.008]
                 [16]   Ding ZH, Zhou Y, Zhou MC. Modeling self-adaptive software systems by fuzzy rules and Petri nets. IEEE Trans. on Fuzzy Systems,
                     2018, 26(2): 967–984. [doi: 10.1109/TFUZZ.2017.2700286]
                 [17]   Maia PH, Vieira L, Chagas M, Yu YJ, Zisman A, Nuseibeh B. Dragonfly: A tool for simulating self-adaptive drone behaviours. In: Proc.
                     of the 14th IEEE/ACM Int’l Symp. on Software Engineering for Adaptive and Self-managing Systems (SEAMS). Montreal: IEEE, 2019.
                     107–113. [doi: 10.1109/SEAMS.2019.00022]
                 [18]   Zhang  QY.  Design  and  implementation  of  workflow  integrated  with  rule  engine  on  SaaS  platform  [MS.  Thesis].  Beijing:  Beijing
                     University of Posts and Telecommunications, 2017 (in Chinese with English abstract).
                 [19]   Jiang YP, Wang W, Shi BL, Dong JR. Model and behavioral determinism theory for ECA rules. Ruan Jian Xue Bao/Journal of Software,
                     1997, 8(3): 190–196 (in Chinese with English abstract). https://www.jos.org.cn/jos/article/abstract/19970305
                 [20]   Vlahavas I, Bassiliades N. Parallel, Object-oriented, and Active Knowledge Base Systems. New York: Springer, 1998. [doi: 10.1007/978-
                     1-4757-6134-4]
                 [21]   Zhang Z. Research on technologies of rules in spatial database [MS. Thesis]. Changsha: National University of Defense Technology,
                     2010 (in Chinese with English abstract).
                 [22]   Hu  LP,  Liang  XL,  He  LL,  Zhang  JQ,  Ren  BX,  Qi  D.  Construction  method  of  aviation  swarm  decision  rule  base  based  on  scenario
                     analysis. Acta Aeronautica et Astronautica Sinica, 2020, 41(S1): 723737 (in Chinese with English abstract). [doi: 10.7527/S1000-6893.
                     2019.23737]
                 [23]   Li  QS,  Wang  L,  Chu  H,  Zhang  M.  Agent-based  software  adaptive  dynamic  evolution  mechanism.  Ruan  Jian  Xue  Bao/Journal  of
                     Software, 2015, 26(4): 760–777 (in Chinese with English abstract). http://www.jos.org.cn/1000-9825/4757.htm [doi: 10.13328/j.cnki.jos.
                     004757]
                 [24]   Moreno G, Kinneer C, Pandey A, Garlan D. DARTSim: An exemplar for evaluation and comparison of self-adaptation approaches for
                     smart cyber-physical systems. In: Proc. of the 14th IEEE/ACM Int’l Symp. on Software Engineering for Adaptive and Self-managing
                     Systems (SEAMS). Montreal: IEEE, 2019. 181–187. [doi: 10.1109/SEAMS.2019.00031]
                 [25]   Liu DW, Yang YJ. Particle swarm algorithm based on chaos local search and its application. Computer Technology and Development,
                     2021, 31(4): 216–220 (in Chinese with English abstract). [doi: 10.3969/j.issn.1673-629X.2021.04.037]
                 [26]   Wang L, Huo QE, Li QS, Wang Z, Jiang YX. Self-adaptation decision-making based on parallel search optimization for command and
                     control information system. Ruan Jian Xue Bao/Journal of Software, 2022, 33(5): 1774–1799 (in Chinese with English abstract). http://
                     www.jos.org.cn/1000-9825/6561.htm [doi: 10.13328/j.cnki.jos.006561]
                 [27]   Wu T, Li QS, Wang L, He L, Li YJ. Using reinforcement learning to handle the runtime uncertainties in self-adaptive software. In: Proc.
                     of the 2018 STAF Collocated Workshops on Software Technologies: Applications and Foundations. Toulouse: Springer, 2018. 387–393.
                     [doi: 10.1007/978-3-030-04771-9_28]
                 [28]   Bahrampour S, Ramakrishnan N, Schott L, Shah M. Comparative study of deep learning software frameworks. arXiv:1511.06435, 2016.
                 [29]   Verstaevel N, Boes J, Nigon J, d’Amico D, Gleizes MP. Lifelong machine learning with adaptive multi-agent systems. In: Proc. of the 9th
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