A Rationale-based Model for Architecture Design Reasoning

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10.3. Reasoning about change impact with AREL is most influential because if we change the fundamental design of asynchronous processing, say to synchronous, then the assumption of how to detect loss of acknowledgement messages would change and the subsequent design would also be different. This is reflected in the CPT of AR14 where the probability of C4 2 4 being volatile whilst the other requirements being stable is 0.3. C4 2 4 also affects decisions AR13 and AR16, their CPTs are shown in Figure 10.5(b). Figure 10.5(b) shows the prior probabilities and the CPTs of the security design for payment message processing. There are a number of key decisions, AR13 is a decision on the processing steps of MCP. It is influenced by C4 2 17 because it must be decrypted before the message can be processed. It is influenced by C4 2 5 because the message must then be validated. If validation fails, the message is sent back to the originator with a negative acknowledgement immediately. The decision is influenced by C4 2 4 because this decision is based on the assumption that the treatment of acknowledgement is asynchronous, therefore MCP must be stateful, i.e. MCP has to remember the status of a payment message so that when the acknowledgement is returned at some point in the future, it could be matched to the original message. AR16 is a decision on processing acknowledgement. It depends on a number of factors including C4 2 4, C4 2 5 and C4 2 17. The decision relies heavily on C4 2 4 in that if the acknowledgement processing model changes, it is highly likely that the decision will become invalid. 10.3.2 Original beliefs modelled by AREL The AREL model in Figure 10.6 is a representation combining the illustrations in Figure 10.5(a) and 10.5(b) without the details of the prior probabilities and CPTs. It shows the marginal probability distribution when no evidence (i.e. change) has been added. The specific visualisation format is provided by Netica [107] BBN software. The visualsation includes the name of each node across the top, the state names are on the left hand side of the nodes (valid/invalid for AR nodes and stable/volatile for AE nodes). Marginal probabilities are shown as percentages (e.g. 85.6% and 14.4% for AR10 ) by the two horizontal bars. The marginal probabilities, for instance, of a leaf-node such as C4 2 9, are calculated based on its own CPT and the CPTs and prior probabilities distributions of its ancestors. For instance, the marginal probability of C4 2 9 indicates that based on the current requirements and design, there is a probability of 0.70 (shown as 70% in Figure 10.6 by Netica) that the design is stable. 181
10.3. Reasoning about change impact with AREL Figure 10.6: An Example BNN Shown with Beliefs Before Any Evidence is Entered 10.3.3 Predictive reasoning As the requirement or the environment in the system changes, we want to predict their effect in terms of possible architecture design changes. Being able to identify the areas that are likely to change would improve both the accuracy and efficiency of architecture enhancements. Recall from Section 10.2 that reasoning in a BBN means users first set the values of certain nodes in the network to indicate evidence that has been gathered about the system. For the AREL BBN model, setting values means that change takes place in the system. These changes are then propagated through the network, producing a new probability distribution over the remaining variables in the network [83], showing the what-if scenarios of the impact of change. Multiple changes could be introduced simultaneously. Let us consider how we can do this type of predictive reasoning in the BBN representation of AREL. To add one or more likely changes as evidence to the AREL model, an architect would instantiate the BBN with evidence that an AE is “volatile”, i.e. volatile state is et to 100%. The BBN belief updating algorithms would then compute the posterior probabilities for AR and AE nodes which are affected by the additional evidence in the network. The architect can assess the change impacts based on the posterior probabilities. In particular, which decisions (AR nodes) are more likely to be invalid and which architecture elements (AE nodes) are more likely to be volatile. 182
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A Rationale-based Model for Archite
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maintenance. These techniques can h
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Declaration This thesis contains no
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2.2.6 DoD Architecture Framework .
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5.4.6 Risk assessment in architectu
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8.2 The architecture rationalisatio
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12.2 Contributions . . . . . . . .
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6.10 ≪AR≫ and ≪AAR≫ Stereot
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11.13A Process to Synchronise Chang
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5.13 Design Rationale Helps Evaluat
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that architecture design is highly
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1.2. Research motivations and resea
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1.2. Research motivations and resea
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1.4. Structure of the thesis the ch
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Part I Problem Analysis 10
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2.1. Industry practice of design ra
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2.2. Architecture frameworks and de
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Chapter 3 Related work in design ra
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3.2. Why do we need design rational
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3.2. Why do we need design rational
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3.3. Existing methods for capturing
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3.4. Other related studies 3.3.7 Qu
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3.4. Other related studies evolutio
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3.5. How well design rationale meth
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3.5. How well design rationale meth
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Chapter 4 Research methodology and
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4.1. Software engineering research
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4.2. The chosen research and valida
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Part II Architecture Design Rationa
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on a regular basis. Their inputs ha
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5.1. Architecture rationale in the
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5.2. Survey methodology data collec
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5.3. Survey findings experience). I
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5.3. Survey findings Table 5.1: Fre
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5.3. Survey findings Business Goals
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5.3. Survey findings 5.3.5 Document
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5.3. Survey findings Table 5.8 summ
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5.3. Survey findings We used Spearm
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5.3. Survey findings previous desig
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5.4. Discussion of findings Since t
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5.4. Discussion of findings or cons
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5.5. Limitations and use design rat
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Part III The Representation and App
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6.1. A conceptual model for design
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6.2. Architecture Rationale and Ele
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6.2. Architecture Rationale and Ele
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6.3. Architecture elements Figure 6
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6.4. Architecture rationale Figure
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6.4. Architecture rationale • ris
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6.4. Architecture rationale depicts
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6.5. The extended AREL To prevent c
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6.6. A UML representation of AREL a
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6.6. A UML representation of AREL a
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6.7. AREL usability AE by ≪AEsupe
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6.8. Summary The AREL model is exte
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7.1. The EFT system reasoning, a co
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7.1. The EFT system The processing
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7.1. The EFT system not chosen beca
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7.1. The EFT system Figure 7.5: Int
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7.1. The EFT system sequence checki
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7.1. The EFT system Figure 7.7: Dec
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7.1. The EFT system Therefore, in a
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7.1. The EFT system Figure 7.10: De
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7.2. An empirical study to validate
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Page 215 and 216: 11.2. Checking AREL models Figure 1
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Page 233 and 234: BIBLIOGRAPHY [10] B. W. Boehm, Soft
Page 235 and 236: BIBLIOGRAPHY [37] A. Eden and R. Ka
Page 237 and 238: BIBLIOGRAPHY [62] J. D. Herbsleb an
Page 239 and 240: BIBLIOGRAPHY [89] N. Lassing, D. Ri
Page 241 and 242: BIBLIOGRAPHY [115] D. L. Parnas,
Page 243 and 244: BIBLIOGRAPHY [142] Z. Simsek and J.
Page 245 and 246: BIBLIOGRAPHY [167] W. F. Tichy, N.
Page 247 and 248: Appendix A AREL Tool User Manual 1
Page 249 and 250: 4.1 AREL Check Model When the Check
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4.2 AREL Model Tracing AREL model t
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Figure 9 - result of downward trace
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To initiate the graph’s creation,
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onto the diagram 14 Assign values t
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Useful Definitions The following ar
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) Some projects only c) Not at all
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19. I revisit architecture design d
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Complaint Procedure If you have any
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Privacy Protection Only investigato
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B. Exploring design reasoning. 1. W
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3. Why does the system use asynchro
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C. General Comments on AREL Modelli
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IEEE Computer Society Press. A. Tan
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