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178 ENTERPRISE INFORMATION SYSTEMS VI assumption is made about what characteristics the actual components must possess from the architecture’s perspective. Thus, the specification of the architecture A (SA) defines a specification SC for the abstract component type C (i.e. SA � SC). Any component Ki, that is a concrete instance of C, must conform to the interface and behaviour specified by SC. We should remark the importance of determining behavioural incompatibilities through the use of scenario specifications, even thought the scenario S is not explicitly included into our measure definitions. This is due to the fact that we consider the definition of metrics as a process included into a broader measurement process, which defines some activities for setting the measurement context – such as defining scenario specifications or identifying stakeholders (Cechich and Piattini, 2003). Determining the needed quality is difficult when different stakeholders have different needs. One might be tempted to state that the stakeholder requiring the highest component quality should determine the overall level of quality to the CBS. But what if that component use is rather minor and unimportant to the CBS, whereas the major component use does not require anywhere near such a level of quality? Thus it is necessary to balance conflicting requirements for CBS quality. Weighting requirements for COTS component selection can be problematic. Sometimes these weights are inconsistent and lead to confusion about which are the most essential customer requirements (Maiden and Ncube, 1998). Using more sophisticated methods, such as the AHP method (Saaty, 1990), has received some interest in the application of well-known COTS component selection procedures. However, simpler decisionmaking techniques can also be appropriate to resolve disagreements promoting a cost-effective use. In any case, clearly defining a way of balancing preferences on requirements is essential to the selection process. On the other hand, the requirements elicitation techniques have widely used a family of goaloriented requirements analysis (GORA) methods – I* (I* homepage; Mylopoulos et al., 1999), KAOS (KAOS homepage; Dardenne et al., 1993), and GRL (GRL homepage) – as an approach to refine and decomposing the needs of customers into more concrete goals that should be achieved. In this paper, we describe a proposal to balance stakeholder’s preferences during a COTS component selection process. Our proposal extends a version of a Goal-Oriented Requirements Analysis Method called AGORA (Kaiya et al., 2002) by considering additional features of COTS components. The proposal might be combined with other techniques for weighting preferences such as the Goals-Skills- Preferences Framework (Hui et al., 2003) and the AHP method. Then, the balanced requirements are included into the computation of a compact suite of measures on functional suitability of the COTS component candidates. In Section 2 of the paper we briefly introduce our measurement approach for COTS component’s functional suitability. Section 3 then presents the notion of AGORA graphs as it might be used in COTS component selection to balance stakeholder’s preferences. Finally, section 4 introduces our weighting procedure to functional suitability based on measures derived from the graph. We conclude with an overview of research directions and future extensions. 2 MEASUREMENT OF COTS FUNCTIONAL SUITABILITY In the previous section, we have emphasized the fact that the output from the system should satisfy the user’s requirements by using the functionality supplied by at least one COTS component. They are plugged into a software architecture that connects participating components and enforces interaction rules. Given a specification SC for an abstract component type C, a candidate component K to be a concrete instance of C must conform to the interface and behaviour specified by SC. Although the process of selecting a component K consists of evaluating interface and semantic mappings, in our work only semantic mappings are addressed. Mappings in SC, which represent the different required functionalities, are established between input and output domains. We focus on incompatibilities derived from functional differences between the specification in terms of mappings of a component Ki (SKi) and the specification in terms of mappings of SC. Let’s illustrate the measurement procedure by using an E-payment system as an example. We suppose the existence of some scenarios describing the two main stages of the system – authorisation and capture. Authorisation is the process of checking the customer’s credit card. If the request is accepted, the customer’s card limit is reduced temporarily by the amount of the transaction. Capture is when the card is actually debited.
The scenarios will provide an abstract specification of the mappings of SC that might be composed of: - Input domain: (AID) Auth_IData{#Card, Cardholder_Name, Exp-Date}; (CID) Capture_Idata{Bank_Acc, Amount}. - Output domain: (AOD) Auth_Odata{ok- Auth}; (COD) Capture_Odata{ok_capture, DB_update}. - Mapping: {AID � AOD};{CID � COD} Suppose we pre-select two components to be evaluated, namely K1 and K2 respectively. A typical situation for inconsistency in the functional mappings between SK1, SK2 and SC is illustrated in Figure 1, where dashed lines indicate (required) mappings with respect to SC, and the solid lines are (offered) mappings with respect to SK1 (grey) and SK2 (black). Note that the input domain of the component K1 does not include all the values that the specification SC requires, i.e. the capture functionality is not provided. Besides, the input domain of the component K2 includes more values than the required by SC, although the mapping satisfies the required functionality. We should also note that there is another functionality provided by K2, i.e. {Taxes � Statistics}, which might inject harmful effects to the final composition. • Taxes BALANCING STAKEHOLDER'S PREFERENCES ON MEASURING COTS COMPOENT FUNCTIONAL S C(i) dom S K2 dom S C dom SK1 • AID • CID S Ki(i) S K2(i) • Statistics ran SK2 ran SK1 • AOD • COD ran SC Figure 1: Functional mappings of S C and S K1/S K2 Our measures of functional suitability have been classified into two different groups: component-level measures and solution-level measures. The first group of measures aims at detecting incompatibilities on a particular component K, which is a candidate to be analysed. However, it could be the case that we need to incorporate more than one component to satisfy the functionality required by the abstract specification SC. In this case, the second group of measures evaluates the functional suitability of all components that constitute the candidate solution. To clarify the use of the specification SC during the measurement procedure, we briefly introduce some metrics. At the component-level, we have defined the following measures (Cechich and Piattini, 2004): �� “Compatible Functionality” (CFC) as the amount of functional mappings provided by SK and required by SC in the scenario S. �� “Missed Functionality” (MFC) as the amount of functional mappings required by SC in the scenario S and not provided by SK. �� “Added Functionality” (AFC) as the amount of functional mappings not required by SC in the scenario S and provided by SK. �� “Component Contribution”(CCF) as the percentage in which a component contributes to get the functionality required by SC in the scenario S. Now, let’s calculate the functional suitability measures on K2 for the E-payment example. Considering the functional mappings provided by K2 ({AID � AOD; CID � COD; Taxes � Statistics}), the component-level measure results are as follows: CFC(K2) = 2; MFC(K2) = 0, AFC(K2) = 1; CCF(K2) = 1. 179 These values indicate that the component K2 is a candidate to be accepted for more evaluation; i.e. the component is completely functionally suitable. But there is one added function that could inject harmful side effects into the final composition. Besides, there are another types of analysis the component should be exposed before being eligible as a solution – such as analysis of non-functional properties (Chung et al., 2000), analysis of vendor viability (Ballurio et al., 2002), and so forth. Adaptation required by the components should also be quantified. For example, measurement might be defined at three levels: (1) size measures will be basically in terms of the amount of adaptability needed by a component-based solution; (2) complexity of adaptation will be measured in terms of interactions with target components that are identified to determine all potential mismatches; and finally, (3) architectural adaptability might define calculations for measures of changes that affect system’s stability (Cechich and Piattini, 2003) in terms of architectural adaptability.
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vi TABLE OF CONTENTS PART 1 - DATAB
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viii TABLE OF CONTENTS A WIRELESS A
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CONFERENCE COMMITTEE Honorary Presi
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Hung, P. (AUSTRALIA) Jahankhani, H.
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Invited Papers
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2 ENTERPRISE INFORMATION SYSTEMS VI
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10 ENTERPRISE INFORMATION SYSTEMS V
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EVOLUTIONARY PROJECT MANAGEMENT: MU
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MANAGING COMPLEXITY OF ENTERPRISE I
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ASSESSING EFFORT PREDICTION MODELS
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ORGANIZATIONAL AND TECHNOLOGICAL CR
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ASAP Processes W Project Kickoff A
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since it means changing the relevan
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ACME-DB: AN ADAPTIVE CACHING MECHAN
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ACME-DB: AN ADAPTIVE CACHING MECHAN
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Hit Rate (%) ACME-DB: AN ADAPTIVE C
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5.3.6 Effect on all Policies by Int
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ACKNOWLEDGEMENTS We would like to t
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This paper describes the data sampl
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The major limit A of the operation
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RELATIONAL SAMPLING FOR DATA QUALIT
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TOWARDS DESIGN RATIONALES OF SOFTWA
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((Brown et al., 1998), pp. 159-190,
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sive data filtering, presentation (
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• implementation changes in servi
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MEMORY MANAGEMENT FOR LARGE SCALE D
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Data Rate [bytes/sec] 1600000 14000
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E.g., DV Camcorder Camera Packets (
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Procedure CheckOptimal (n rs 1 ...n
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MEMORY MANAGEMENT FOR LARGE SCALE D
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PART 2 Artificial Intelligence and
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110 ENTERPRISE INFORMATION SYSTEMS
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DYNAMIC MULTI-AGENT BASED VARIETY F
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Page 169 and 170: AN EXPERIENCE IN MANAGEMENT OF IMPR
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CABA 2 L A BLISS PREDICTIVE COMPOSI
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Figure 4: Symbols emission in DAR-H
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they evidence a generalization erro
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A METHODOLOGY FOR INTERFACE DESIGN
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and mobility loss coupled with redu
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Tradeoff: The default input is poss
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Sessions on the VABS which are held
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A CONTACT RECOMMENDER SYSTEM FOR A
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A CONTACT RECOMMENDER SYSTEM FOR A
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- "Going to the sources". The user
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Here are the matrix W and P for our
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EMOTION SYNTHESIS IN VIRTUAL ENVIRO
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theme turns out to be surprisingly
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6 FROM FEATURES TO SYMBOLS 6.1 Face
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sions are described by different em
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Electronic Imaging 2000 Conference
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to the accessibility problem for th
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Figure 3: Hierarchical View of the
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4 CONCLUSIONS AND FUTURE WORK On th
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PERSONALISED RESOURCE DISCOVERY SEA
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profile, the user defines his curre
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Abdelkafi, N. .....................
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