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APPENDIX E <strong>D.3.3</strong> Algorithms for Incremental Requirements Models Evaluation and Transformation| version 1.19 | page 85/136
Noname manuscript No. (will be inserted by the editor) Dealing with Known Unknowns: A Goal-based Approach for Understanding Complex Systems Evolution ⋆ Fabio MASSACCI, Le Minh Sang TRAN Università degli Studi di Trento, I-38100 Trento, Italy e-mail: surname@disi.unitn.it The date of receipt and acceptance will be inserted by the editor Abstract Enterprises must cope with evolution to addresses changing business objectives, changing regulations and changing threats. In many cases such changes are not completely unknown: the ongoing discussion of a standard body can show that 2-3 proposals might emerge, albeit might not be clear which one will finally win. In this paper we tackle the fundamental issue of modeling and reasoning about the future evolution of the business and security goals of an organization in presence of uncertainty of known outcomes. This work describes a generic approach explicitly representing goal evolutions in terms of controllable and observable rules and in which probability estimates can be validated by a game-theoretic semantics between stakeholders and nature. In this setting it is important to identify which are the business activities that must be implemented to guarantee the best chances of success (maximum belief) or minimize the risk of wasting money (residual risk). We specialize the set-up for a goal-based language where we provide a graphical language but also efficient algorithm that allows to reason about the unrolling of probabilities without a combinatorial explosion. In order to illustrate the applicability of the approach we discuss a case study from Air Traffic Management for the deployment of AMAN in air-traffic control procedures. ⋆ It is an extended and revised version of [38]. It is partly supported by the European Commission under projects EU-FET-IP-SECURECHANGE, and EU-IP-NESSOS. We would like to thank F. Paci at the University of Trento, A. Tedeschi, V. Meduri, M. Felici, S. Pozzi at DBL Srl and the other participants to the ATM validation sessions for many useful comments. The numbers presented in this paper are only provided as examples and do not represent in any way the opinion of the experts’ organizations.
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D.3.3 ALGORITHMS FOR INCREMENTAL RE
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1.14 19 January Final 1.15 23 Janua
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Index DOCUMENT INFORMATION 1 DOCUME
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1 Introduction This deliverable pre
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(Clause 6.4.3) processes of ISO/IEC
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3 Orchestrating Requirements and Ri
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grey. The ADS-B introduction requir
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The security risk manager identifie
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are supported by the argumentation
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ADS-B Manage ADS-B signal ADS-B sig
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the stagnation suite (i.e. obsolete
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5 A framework for modeling and reas
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sectors. And there is 42% of probab
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SDA(C) = {DA 2 , DA 4 , DA 8 , DA 1
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application contexts is saved if th
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Figure 14. ATM model state after qu
Page 33 and 34: References [1] Yudis Asnar, Fabio M
Page 35 and 36: Managing Changes with Legacy Securi
Page 37 and 38: Failure in the provisioning of corr
Page 39 and 40: propagated to the system designer a
Page 41 and 42: APPENDIX B D.3.3 Algorithms for Inc
Page 43 and 44: is sometimes difficult, especially
Page 45 and 46: Tactical Monitor air R controller t
Page 47 and 48: protected from harm. Since in CORAS
Page 49 and 50: CModel. The postcondtion checks the
Page 51 and 52: of ADS-B signal and Availability of
Page 53 and 54: extensions to i* to model and analy
Page 55 and 56: 3. Bzivin, J., Dup, G., Jouault, F.
Page 57 and 58: APPENDIX C D.3.3 Algorithms for Inc
Page 59 and 60: steps. Section V demonstrates the R
Page 61 and 62: that should be mitigated by the sys
Page 63 and 64: CPU value PINconfirmed(PIN, card-de
Page 65 and 66: TABLE IV PRIORITIZATION OF RISKS FO
Page 67 and 68: context. We believe that the Common
Page 69 and 70: Managing Evolution by Orchestrating
Page 71 and 72: 1.1 The Contribution of this Paper
Page 73 and 74: Fig. 3. Integrated Change Managemen
Page 75 and 76: The requirement analysis is an iter
Page 77 and 78: Table 1. Conceptual Interface Requi
Page 79 and 80: Legend: Goals surrounded by dashed
Page 81 and 82: Table 5. Test Suite for GP-2.2 Tran
Page 83: 6. P. K. Chittimalli and M. J. Harr
Page 87 and 88: Dealing with Known Unknowns: A Goal
Page 113 and 114: Understanding How to Manage Require
Page 115 and 116: Chatzoglou et al. [4] conducted a s
Page 117 and 118: Evolution Elicitation Probability E
Page 119 and 120: Table 2. Participants Background Pa
Page 121 and 122: for the defined success criteria. O
Page 123 and 124: Fig. 3. Codes Coverage ATM systems,
Page 125 and 126: The feedbacks provided by the ATM e
Page 127 and 128: 3. P. Carlshamre, K. Sandahl, M. Li
Page 129 and 130: Quick fix generation for DSMLs Ábe
Page 131 and 132: • extensibility: the supported li
Page 133 and 134: Fig. 7. Overview of the Quick fix g
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Fig. 9. Evaluation results (|V(M I
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