SEKE 2012 Proceedings - Knowledge Systems Institute

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may contain only one class/interface definition; (2) a Java file may contain several class/interface definitions; (3) a class may contain inner class definitions. For the mapping result of the element IType, the IType can be extracted and used to form a new ICompilationUnit which contains this IType as the only element. An artifacts of the type I ImportDeclaration, IField, IInitializer or IMethod is seldom used as analysis input because its definition is meaningful only in the context of an IType. For the mapping result of the type IField or IMethod, we extract its parent element which is of IType as the analysis artifact. TABLE I. THE MAPPING RULES UML Pre-condition Mapping Eclipse artifact Element type Project -- 1-1 IJavaProject Package -- 1-1 IPackageFragment Class/ If the ICompilationUnit 1-1 ICompilationUnit Interface contains only one IType which is of the same name If the ICompilationUnit 1-1 IType contains more than one IType, or the IType is child of another IType Function -- 1-1 IMethod Variable -- 1-1 IField In the integration stage, the integrator describes the required input of analysis method. We provide option list based on the artifact formats used in a Java application. If there is mismatch between the artifact set and the analysis input, we choose the analysis input which is the maximum subset of the artifact set compared to other validate analysis inputs included in the artifact set. Because artifacts are arranged as a tree structure, the input is adapted by a breadth-first search of analysis input from the root of the tree. B. Analyzer Execution In the input adaptation stage, analysis inputs are generated according to the input requirements. In the analyzer execution stage, the analysis task includes providing input for the analyzer, running the analyzer, and acquiring the analysis results. There are two kinds of analysis execution requirements: automatic analysis and analysis with user interaction. Generally, the goal of analysis is to acquire the analysis results for use. This kind of analysis requires analyzing the input without user interaction. However, with the increasing analysis complexity, some analysis methods may require user configuration during analysis based on some intermediate results. This kind of analysis methods are usually implemented with graphical user interface so as to help understanding the intermediate and final results. Considering these two kinds of analysis requirements, our framework provides infrastructures for integration of analyzer with command line interface and analyzer implemented as Eclipse plug-in, respectively. Analysis tools with command line interface are featured by its structured commands and black-box-like execution. The tool can be executed using command following pre-defined structure. The command structure usually includes the command name with several parameters, such as input and output. In addition, some more parameters may be needed to drive the analyzer in different modes. For the integration of this kind of analyzer, we provide #INPUT# and #OUTPUT# to stand for the input and output in the command string. The analy sis integrator can define the command structure using these par ameters. To drive the analyzer, our framework generates a con crete command with the input and the output of certain analysis task. The #INPUT# will be replaced by the path of the artifact found in the input adaptation. The output path can b e user-defined or using default value generated by our framework. In some cases, we find that the analyzer’s command structure does not include the parameter for analysis output and its analysis results are returned in standard output stream which is printed in texture terminal. In this case, our framework saves the output as a file in the output file path through output redirection. Take the integration of an analysis method JDepend [9] as an example. Its command structure is defined as “java jdepend.xmlui.JDepend -file #OUTPUT# #INPUT#”. When running the analyzer, our framework generates a command “java jdepend.xmlui.JDepend -file C:\result C:\input” if the input path is “C:\input” and the result path is “C:\result”. For an analyzer implemented as Eclipse plug-in, it usually provides graphical user interface and supports user interaction during analysis. A plug-in has a unique identifier registered in Eclipse plug-in registry. We assume that an analysis method in the plug-in can accept command triggered by end-user from an item in the user interface. Considering the Eclipse’s plug-in mechanism, the command activating an analysis method is called an ac tion. A plug-in may include several analysis methods and provide the invocation interfaces of different analysis methods through different actions. It should be noted that not all the actions provide analysis service. The definition for analyzer execution includes the plug-in identifier, the extension type and the action class name. The integration framework requires the installation of plug-in analyzer before using the analysis method. When an analysis method is started, our framework finds the plug-in according to its unique identifier. And then, our framework instantiates an extension object of the extension type to run the action. Because the E clipse platform loads all its plug-ins in a lazy manner, the integrated analysis methods will not be instantiated and activated until it is used. It means that the integrated plugins will not affect the performance of Eclipse platform. When execution of an analysis method is finished, the relationship between user input in UML model and analysis result is recorded and stored in an XML file. The traceability information is provided for integration of analysis results. C. Analysis Result Integration based on Traceability Information Analysis is th e process of breaking a complex topic or substance into smaller parts to gain a better understanding of it. In an analysis method, its input is the analysis object, and the analysis result is the property of the ana lysis object. Because the analysis input can be abstracted as elements in the software model, the analysis result can be abstracted as the property of an element or a set o f elements. The goal of analysis result integration is t o build the relationship between the software 213
model and the analysis results. The integration is based on the traceability information recorded during analysis process. The traceability information from the analysis object selected in the UML model, the artifact set, the analysis inputs and analysis results is kept by the integration framework during input adaptation and runtime execution stage. Relationship between UML model and the input is decided by the platformspecific rules and the required input format of the analysis method. During analyzer executeion, relationship is recorded into an XML file. After these two st eps, the relationship between UML model and analyzer output can be deduced. The traceability recording process is shown in Fig. 2. Artifacts Artifact set (for analysis) Analysis input Analysis input Analysis input Analysis Result Analysis Results Analysis Results Figure 2. Traceability recording Model Model Element Model Element Model Element Model Element Model Element Model Element Properties of Properties of Model Properties Elementof Model Element Model Element With the traceability information recorded, the UML model will be e xtended with the analysis results as attributes of the analysis objects. To display the integrated analysis results, we provide a friendly user interface to show relationship between UML models and analysis results. In the UM L diagrams, the extended properties will be linked to the analysis results. IV. CASE STUDIES We implement the supporting tool of our framework as an Eclipse plug-in. Using the tool, we have already integrated 13 analysis methods. We first in troduce the integrated analysis methods, and then use an analysis method JDepend[9] as an example to introduce how to integrate an analysis method and how to use an integrated analysis method in our framework. A. The Integrated Analysis Methods We have already integrated 13 analysis methods in our framework. The in tegrated analysis methods are shown in Table 2. We assume that the analysis method integrators are analysis method developers who can provide detail information about the a nalysis methods. As a resul t, we choose analysis methods with open source analyzers, so as to provide de tail integration information. In the table, we can see th at an ana lyzer may implement one or more analysis methods. An an alysis method is a relatively standalone functional unit providing analysis service in the analyzer. There may be several analysis methods in its inner structure. An analysis method has its specific input format requirement. However, in the table, we co mbined the analysis methods with the same invocation interface but different input requirements as one analysis method for space limit. TABLE II. INTEGRATED ANALYSIS METHODS Analysis method Analyzer Input Format Analyzer implementation JDepend JDepend [9] IPackageFragmentRoot/ Eclipse plug-in/ IPackageFragment Command line Classycle Classycle IJavaProject Eclipse plug-in/ [13] Command line Code Analysis CodePro Analytix [14] IPackageFragmentRoot/ IPackageFragment/ ICompilationUnit Eclipse plug-in Similar Code Analysis Metrics Code Coverage Dependenc y Analysis Findbugs Checkstyle CodePro Analytix CodePro Analytix CodePro Analytix CodePro Analytix Findbugs [15] Checkstyle [16] IPackageFragmentRoot/ IPackageFragment/ ICompilationUnit IPackageFragmentRoot/ IPackageFragment/ ICompilationUnit IPackageFragmentRoot/ IPackageFragment/ ICompilationUnit IPackageFragmentRoot/ IPackageFragment/ ICompilationUnit IJavaProject/ IPackageFragmentRoot/ IPackageFragment/ ICompilationUnit IJavaProject/ IPackageFragmentRoot/ IPackageFragment/ ICompilationUnit PMD PMD[17] IPackageFragmentRoot/ ICompilationUnit Yasca Yasca [18] IPackageFragmentRoot/ ICompilationUnit JLint JLint [19] IPackageFragmentRoot/ ICompilationUnit JavaNCSS JavaNCSS IPackageFragmentRoot/ [20] ICompilationUnit Eclipse plug-in Eclipse plug-in Eclipse plug-in Eclipse plug-in Eclipse plug-in Eclipse plug-in /Command line Command line Command line Command line Command line B. Integrating the JDepend Analysis Method JDepend[9] is an open source analysis tool for Jav a application. Its analysis result is design qu ality metrics. The metrics include number of classes and interfaces, afferent couplings, efferent couplings, abstractness, etc. I t has two versions of implementations and could be run as Eclipse plugin or from command line interface. In this section, we use JDepend as an example to introduce how to integrate analysis method and use integrated analysis method in our framework. (1) Integrating an analysis method The JDepend integrator provides the integration definition. The elements of integration definition are shown in Tab le 3. The elements analysis method plug-in identifier, type of extension point, and plug-in action class are used for integrating analyzer implemented as Ec lipse plug-in. The element command structure is used for integrating analyzer with command line interface. It should be noted that the elements for inte grating analyzers are optional. An analysis method can be integrated with any kind o f analyzer implementations. TABLE III. Element Name Analysis Method Name Required input of analyzer THE INTEGRATION DEFINITION Description The name of analysis method being integrated The input format accepted by the analyzer. A list of supported formats are provided. 214
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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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One of the st udents from Group B t
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scribed by the equation: where η i
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4 Model Checking DPN We use HyTech
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Recommender systems are usually cla
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such as one week and gradually dete
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•
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146
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Enforcing Contracts for Aspect-orie
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Table 1. Behavioral Rules in LSD [9
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1 public privileged aspect Update {
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Towards More Generic Aspect-Oriente
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vals must be denoted by some notion
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Aspect-Orientation in the Developme
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Table II SELECTED PRIMARY STUDIES #
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Page 209 and 210: An Approach for Software Component
Page 211 and 212: properties. In particular, the foll
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Page 215 and 216: equipment maintenance node may cont
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Page 229 and 230: Choosing licenses in free open sour
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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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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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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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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
Page 727 and 728:
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
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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
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information. If more than one adver
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Users can al so publish adv ertisem
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The COIN Platform: Supporting the M
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A-3
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Checking Contracts for AOP using XP
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Maicon B. da Silveira, 112 Aldo Dag
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Seyedehmehrnaz Mireslami, 70 Takao
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Wei Zhang, 422 Wenbo Zhang, 188 Zhi
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Desmond Greer Eric Gregoire Christi
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Poster/Demo Presenter’s Index A A
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SEKE 2012 Proceedings - Knowledge Systems Institute

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