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and reusable components. This approach can promote the reuse of software components. We can construct the target ontology model by analyzing requirement specification and then calculate the s imilarity between target ontology and source ontology using ontology mapping techniques. Therefore, we can identify the matched source ontology nodes and the c orresponding sets of reusable components and then construct the mapping between the target ontology nodes and t he reusable components. We assume that the mapping between source ontology nodes and reusable components has been realized, so every source ontology node has matched several reusable components. After the ontology mapping, the target ontology nodes also have matched the reusable components through the “ bridge” of the source ontology nodes. Even though function description of customer requirement is the same, the attributes are often different, so it is difficult for each software component to completely meet different requirements of different ontology node. We need to calculate the matching degree between software component and ontology node. We consider the matching degree betw een a concept of the ontology node C and a reusable software component S to be a number P(C, S) between 0 and 1, with 0 representing unmatched and 1 rep resenting completely matched. The formula is: P(C, S) = f (S) / f (C), with f (S) representing the matched functional points nu mber of the curr ent reusable software component, and f (C) representing the total functional points number of the current ontology node. Figure 3. Software component reuse approach based on ontology mapping. CROM Algorithm: Through the above analysis, we find that the mapping between ontology nodes and reusable components is the key to realize the CROM (Component Reuse through Ontology Mapping) algorithm. We can construct the mapping by using requirement engineering. With requirement engineering, we will decompose requirement specification into several fragments and every fragment of requirement contains several functional points. Each functional point contains the input and output, which are designed to match the requirement. The requirement and the corresponding input/output are the basis for the design and i mplementation of software components. Each node of ontology model is related to each fragment of requirement specification, and each fragment is related to several functional points so th at each node of ontology model is al so related to several functional points. This unique approach to construct the mapping between ontology node and reusable components by the functional point is th e heart o f the CROM algorithm. In gen eral, the mapping operation is illustrated in Figure 4. Ontology nodes and reusable components have N:N relationship. Every ontology node may correspond to several reusable components, and every reusable component may correspond to several ontology nodes. Figure 4. The mapping between ontology node and reusable components. Experimental Design: The experiment is divided into two parts. The first part is to construct t he application domain ontology model and th e mapping between the application domain ontology nodes and the reusabl e software components. The dat a source is th e application domain requirement specification and several sets o f reusable software components. In order to make the experiment data more reasonable, we choose three groups ontology model, which are from very similar to th e general ontology to very dissimilar. Firstly, we n eed to construct the application domain ontology model. We adopt the ontological concept to divide the ap plication domain requirement specification into different application fragments. For exa mple, the general equipment application may contain four fragments: equipment, purchase, storage and organization. Every fragment of the requirement contains the co mpletely functional points and input/output and then w e will construct the ontology on th e application domain by processing different fragments describing the application domain and by mining them into common vocabularies as domain-specific concepts. Therefore, every application domain ontology node has several corresponding functional points an d input/output. For exa mple, 185
equipment maintenance node may contain new, updating and delete three functional points. Secondly, we construct the mapping between application domain ontology nodes and reusable software components based on the functional points. We will match the functional points and input/output between every node and t he reusable components by traversing the application domain ontology and then calculating P(C, S). If the number P(C, S) is greater than 0, the reusable component satisfies matching conditions. Because the reusable components are limited, we ca nnot guarantee all of the application domain ontology nodes hav e corresponding components that can be matched. Those nodes will be matched later during the software development and then we can put those components into the reusable components libraries. The second part is to realize the reuse of the software components based o n the o ntology mapping technology and the result of the first part. Firstly, we need t o manually calculate the rate of component reuse for three groups’ ontology model. As previously said, the number P(C, S) represent the matching degree between a concept of the ontology node and a reusable software component. If the number P(C, S) is 1 with the same component and the different ontology model, we can consider that this component is reusable. Apparently, the rate of component reuse is much higher for the similar ontologies. The results are shown in Figure 5. Figure 5. Contrast component reuse rate. Secondly, we need to improve the rate of component reuse by using ontology mapping technology. We choose the similar ontology model for example, application domain ontology and sub-application domain ontology. We need to realize the mapping between sub-application domain ontology and application domain ontology. We can calculate the mapping index according to the proposed approach. The result shows that 45 nodes are equal, 9 no des are similar and 3 no des are not similar. According to the software components reuse ap proach based on the ontology mapping, it is highly likely that the reusable components that match the 45 application domain ontology nodes also match the 45 sub-application domain ontology nodes one by one. Finally, we will check the mapping result between the sub-application domain ontology nodes and t he reusable software components by the functional points. We can calculate the number P(C, S) between sub-application domain ontology nodes and th e reusable components according to the second step of the first part, and then calculate r1, defined as the ratio of reused components over total number of components, to measure the rate of component reuse for the sub-application domain. For those components that are not directly reusable because of different attributes, but with high P(C, S) value, we can adjust the P(C, S) value according to results of the previous step, which was calculated by using th e proposed approach to map the attribute concepts. Then we can calculate r2, the revised rate of component reuse for the sub-application domain. We have developed a software tool to calculate r2. This tool can adjust the number P(C, S) by analyzing the ontology mapping result and component attributes, and then calculate r2. This tool also can compare r1 and r2, and then draw the chart. The results are shown in Figure 6. Figure 6. Improvement in component reuse rate. Through this experiment, we conclude that we can increase the percentage of the co mponents reuse by matching attribute names through ontology mapping. In other words, we can systematically change the na me of variables in a program so that it can be reused in another sub-application domain. We also hav e developed two different components to realize com ponent reuse algorithm and then select better component by using the Slow Intelligence System (SIS) framework [20]. The experiment is divided into three steps. The first step is to implement the super-component that can analyze the ontology mapping result. The super- component includes two components, namely BySAX and ByDOM. T he BySAX component is to parse ontology mapping XML document by SAX (Simple API for XML), which is an event-based sequential access pars er API developed for XML documents. The ByDOM component is to parse ontology mapping XML document by JDOM API, which is a Java-based document object model for XML. In the future, other ontology mapping algorithms can be added to this super-component. The second step is to modify several SIS core java files and config files, so as to support component reuse algorithms. The final step is to run the super-component based on the SIS framework and then obtain the analysis result. The result shows that ByDOM is the better algorithm, whose runtime is 18.54 seconds on a PC. The process is shown in Figure 7. 186
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SEKE San Francisco Bay July 1-3 201
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Copyright © 2012 by Knowledge Syst
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The 24 th International Conference
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Zhenyu Chen, Nanjing University, Ch
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Riccardo Martoglia, University of M
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Poster/Demo Sessions Co-Chairs Fars
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Evaluating the Cost-Effectiveness o
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Program Understanding Evaluating Op
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An Empirical Study of Execution-Dat
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Computer Forensics: Toward the Cons
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Modeling Bridging KDM and ASTM for
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A Mapping Study on Software Product
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A proposal for the improvement of t
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Panel on Future Trends of Software
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een available for analysis. Social
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The rest of this paper is orga nize
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Now we assume is given, we first pr
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“iphone” and “blackberry” a
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nique estimate the impact set based
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Figure 1. An example to illustrate
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trategy, the results should support
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Figure 1. System Architecture would
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Figure 2. Sequence Diagram of Train
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deployed on Android phone and runs
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the ontology information is only us
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Both kinds of axioms lead to an inh
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engineer and achieving requirements
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elicitation and the act ivities con
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Step 2. Step 3. 1.2. Use of Procedu
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Figure 6.b. Task: Cognitive Analysi
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original User’s Discourse / Speec
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II. RELATED WORK Business processes
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test, we did not find any significa
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For the indicator “requirements a
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the model checkers SPIN [8] and NuS
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allow for the presentation of syste
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the requirement or design phases sh
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using the CR estimates against the
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Data Set TABLE II. DATA SETS Artifa
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Figure 4. Relative Error in the Est
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Take basic requirements from user a
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sent Semantic Web Ontologies. It co
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The rest of the paper is organized
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and the risk level. Existing assess
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diagram could give developers an in
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Figure 2. Co
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• Sensor(Lexical): at the top and
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An Overview of the RSLingo Approach
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Figure 2. Overview of the RSLingo a
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DETECTING EMERGENT BEHAVIOR IN DIST
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Definition 2: For a set of MSCs M,
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Stability of Filter-Based Feature S
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Algorithm 1: Threshold-Based Featur
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MI KS GM AUC PRC S2N RUS35 RUS50 RO
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80
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82
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84
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86
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Cloud Application Resource Mapping
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Fig. 1. UML Collaboration Diagram m
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Fig. 2. UML Activity Diagram modeli
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An Empirical Study of Software Metr
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TABLE I SOFTWARE DATASETS CHARACTER
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1) Classifiers: In this study, soft
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Progressive Clustering with Learned
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IV. DATA SOURCES We have been worki
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meta-data we assign to indicate sig
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Multi-Objective Optimization of Fuz
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uilding FNNs that satisfy different
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VI. EXPERIMENTAL RESULTS The presen
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Generating Performance Test Scripts
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test duration(s) for the scenario(s
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which are composed by the name and
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A Catalog of Patterns for Concept L
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assisted solution for lattice analy
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The complete lattice of the class s
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Client-Side Rendering Mechanism: A
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JavaScript code directly within the
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Fig. 6. CPU usage percentage of pre
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may introduce the extra learning cu
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[9] but no validation is provided i
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Page 177 and 178: Enforcing Contracts for Aspect-orie
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Page 247 and 248: II. BACKGROUND In this section, we
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transformation idea in MDA [13] . T
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MappingRule MappingTGtoHP { [Rule I
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executing a continuous transition,
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protocol of DATS we used is properl
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in Boolean search and reasoning has
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to be broken by expelling one of it
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where, among other things: 1. The t
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A never-ending language learner cal
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4. i 2 Learning(DEC, mex) via Bias
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4.4. An Illustrative Example Now le
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Evolutionary Learning and Fuzzy Log
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In addition to the rules, the knowl
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As described in the previous sectio
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the ontology hierarchy; learning a
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three different learning techniques
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TABLE IX. USING SVM FOR LEARNING tr
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Siemens set contains seven C progra
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The program tcas is not included be
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esults. To be more specific, they w
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According to IEEE [10], regression
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(a) Volatility (b) Complexity (c) R
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It mimics the test engineer knowled
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integrate the test results. • Aut
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No tify the cloud controller to get
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of execution-data classification. T
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Figure 1. Boxplots of average AUC r
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anches is more applicable than the
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To validate the proposed approach,
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contains broadcast of Subject to Ob
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S 1a=1; S 2b=5; cobegin { S 3read
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.
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298
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A functionality is the ability of a
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level); in UML/J2EE technology [11]
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• to provide a unified catalogue
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flexibility they provide to model s
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complete in zero-time, and thus hav
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figure assumes the experiment with
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could be a practical way of dealing
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and (iv) the reduced amount of line
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III.BUSINESS RULES FRAMEWORK FOR KN
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explanation for why the program wan
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320
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322
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A model introducing SOAs quality at
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there exist a hierarchical ranking
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Software as a Service: Undo Hernán
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operations consist in reinjection i
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stored. If event failure, external
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ensure users the trustworthiness of
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Patient Doctor Doctor Patient Direc
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going down at a time. This is a rea
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Decidability of Minimal Supports of
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A S 1 1 1 0 0 0 1 1 0 0 0 1 1
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satisfies R( D) R( C) S 1. Compa
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Automated Generation of Concurrent
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target of testing. A blackbox test
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sequence. Each t i θ i in the firi
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SAMAT -AToolforSoftware Architectur
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Figure 4. The SAM Hierarchical Mode
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High-level Petri net Place Place Ty
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verification and thus is often limi
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(r 1 r 2 ...r k ) 1 denotes Path [r
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Fig. 3. Example a 3-compound weakly
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364
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366
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368
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necessary before verification. In t
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CTL formula as: AG (( jp EX( jp ad
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equirements level, like EA-Miner [1
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376
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378
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380
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II. RESOURCE-ORIENTED WORKFLOW MODE
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eventually triggers a co mmon task.
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Input: A resource oriented workflow
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access control exists, the action i
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(ACCDA), is customized to address a
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ICrudSchema and shown in Figure 3.
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Connectors for Secure Software Arch
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Non-repudiation security service pr
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anAsynchronousCustomer InterfaceCon
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How Social Network APIs Have Ended
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OSN Social Feed Unique Features Con
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users can control the reach of thei
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Computer Forensics: Toward the Cons
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In 2009, Cohen et al. in [4] have o
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dcterms:contributor, dcterms:creato
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A Holistic Approach to Software Tra
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Figure 1. Figure 1 shows an overvie
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B. Case study in a proprietary proj
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Pointcut Design with AODL Saqib iqb
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system attributes, methods and exec
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Feature modeling and Verification b
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hasOptionalPart hasPart hasMandato
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A Context Ontology Model for Pervas
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Figure 1. UML view of the model. Th
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Figure 3. Architecture for deliveri
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Ontology-based Representation of Si
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Figure 2. Upper Ontology fragment f
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I2Sim simulation model was transfor
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An ontology-based approach for stor
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fying the equivalences between conc
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• Homonymy conflicts: occur when
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Automatic Generation of Architectur
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we dealt w ith the verticals rules.
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Towards Architectural Evolution thr
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different from replacing it, we dec
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Using FCA-based Change Impact Analy
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Table 2. Impact set of C1,C2,C5 cha
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Figure 3. The PF and PTS results fo
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Forecasting Fault Events in Power D
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which the airport data did not cont
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classified. Recall is the probabili
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Testing Interoperability Security P
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Figure 2. Overview of the Test Gene
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Objective Figure 4. Testing
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A New Approach to Evaluate Path Fea
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k v ( df i ) 0 , it means that the
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Model & Method Feasibility Evaluati
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Structural Testing for Multithreade
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adequate to another criterion. This
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480
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482
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484
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A Tiny Specification Metalanguage W
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C. Syntax Extensions The expressive
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however, does not allow variables f
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derivation dependencies. The third
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model extension as proposed in [6].
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Figure 1. The process for engineeri
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Figure 4. The abstractions extracte
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way to detect something which could
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Algorithm 1: Algorithm to recursive
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obtain the input of experts on desi
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A. Pattern Reuse In patterns reuse
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IV. SCATTER In order to enhance pat
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These latter would be clustered acc
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described or even r etrieved, thus
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DC2AP Element and their Application
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21. Consequences Example of use DC2
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Legacy2Cloud logic. In this scope,
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2.3.1 Complementarity of MDD techno
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3.2.2 EAggregateRelationship The EA
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cess. The proposal follows the ADM
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II. OVERVIEW OF MODAL TRANSITION SY
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(1) V □ (A) ⊆ V □ (B), V ♦
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Suppose P = 〈S P , SP i , AI P ,
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Adaptive software should basically
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A. Step S1 - Define the Business Pr
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F. Step S6 - Monitor at Run-Time Th
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Figure 1, a metamodel defines an XM
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Figure 3 is a feature diagram that
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ecause the system of C-net model is
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Pub-T Tc Tc Sub-T Sub-T Figu
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Visual Studio Achievements, a Visua
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the project. This achievement has t
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proposed in this work requires the
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FTS is an important research area,
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C. Dependent Variables and Measures
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D. Conclusion Validity Conclusion v
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evaluation of team productivity, qu
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Figure 3: Process Fragment Example
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complexity issues traditionally inv
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Fig. 1: Swing Framework Class Diagr
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ize:Class”, with an empty precond
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Fig. 11: Overwritten Method Fig. 12
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Investigating the use of Bayesian n
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process for the network. Thus, this
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Reuse of Experiences Applied to Req
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Step 1: To start the process, the u
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Specification of Safety Critical Sy
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current belief of agents in order t
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Using the Results from a Systematic
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obtained from the categorization of
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that automatically presents the usa
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Improving a Web Usability Inspectio
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Following the experimental methodol
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About the category “Improvements
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Identification Guidelines for the D
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created according to the preview re
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In our analysis, the “Government
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In the following, we present the re
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USE SCENARIO The application Vuelos
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[8] Mayhew, D., “The Usability En
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Interaction State Set Structure Dat
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B. Component Model Fig. 2 show s th
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As a continuation of our research w
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PROMETHEE methods [3] belong to a f
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comparison rule, a value of 1/9 is
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TABLE V: Supermatrix for the exampl
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for knowledge sharing. Based on str
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y applying hash functions to its su
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Empirical Validation of Variability
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III. EXPERIMENTAL STUDY In this sec
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Table III SPEARMAN’S CORRELATION
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A Mapping Study on Software Product
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most relevant sources in software e
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TABLE IV TOOL’S CLASSIFICATION AC
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[2] P. Clements and L. Northrop, So
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and future works. II. BACKGROUND ON
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System Advanced Standard Module A M
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Algorithm 1: Backtracking for MIN-A
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outcomes from such research fields,
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TABLE IV. CONTINUED FROM PREVIOUS P
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contribution of each study was made
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assets, the plugin approach can be
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Figure 3: PlugSPL Feature Model Edi
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contributions from the several acto
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o Guideline 6.4: Softgoal dependenc
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descriptions must also b e configur
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It means that throughout the develo
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B. Instrumentation The experiment w
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• External Validity: W e have con
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II. THE PROPOSED TAXONOMY The taxon
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sub-dimension is categorized as, co
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A Proposal of Reference Architectur
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Figure. 1. Reference Architecture M
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A Variability Management Method for
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C. Correctness of configuration fil
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means patterns which are shown in s
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Tool Support for Anomaly Detection
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Figure 1. System overview of the SD
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Once the datasets were evaluated us
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Reconfiguration of Robot Applicatio
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data dependencies required for keep
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Spacemaker: Practical Formal Synthe
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Fig. 1. The ecommerce object model.
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Fig. 3. Mapping diagram for Figure
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A formal support for incremental be
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machine may be empty which should l
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software, based on revised requirem
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A Process-Based Approach to Improvi
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Appropriate processes m ust support
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Figure 2. Reordered layers of the k
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Automatic Acquisition of isA Relati
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III. VALIDATING THE DIMENSION HEADE
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The table title and the dimension s
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A light weight alternative for OLAP
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i.d. subsets of cubes focused on se
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Match production rules Enterprise S
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A Tool for Visualization of a Knowl
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other ontology visualization tools
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shown at Figure 5. Objectives are s
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Rendering UML Activity Diagrams as
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a = O1 → a → O2 b = O2 → b
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ecordProblem → A → reproducePro
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umlTUowl - A Both Generic and Vendo
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The tool’s ability to deal with S
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Elem. UML Example D 12 Harmonizing
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4) Associations: In the first test
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III. GRAPH REPRESENTATION OF CLASS
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as an add-in to popular UML m odeli
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742
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744
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746
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her necessities. However, the progr
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Figure 3. Global Log. of terms. The
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two stages. The first stage is focu
Page 783 and 784:
754
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756
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758
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With the GDUC approach, we can mode
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Figure 6. Three proposals containin
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764
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766
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Proactive Two Way Mobile Advertisem
Page 799 and 800:
information. If more than one adver
Page 801 and 802:
Users can al so publish adv ertisem
Page 803 and 804:
The COIN Platform: Supporting the M
Page 805 and 806:
A-3
Page 807 and 808:
Checking Contracts for AOP using XP
Page 809 and 810:
Maicon B. da Silveira, 112 Aldo Dag
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Seyedehmehrnaz Mireslami, 70 Takao
Page 813 and 814:
Wei Zhang, 422 Wenbo Zhang, 188 Zhi
Page 815 and 816:
Desmond Greer Eric Gregoire Christi
Page 817 and 818:
Poster/Demo Presenter’s Index A A
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SEKE 2012 Proceedings - Knowledge Systems Institute

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