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Based on the mapping rules in [12], we construct an algorithm to transform activity diagrams to Petri net s and implement in our ver ification tool to provide automatic transformation support. The algorithm is described in List 1. With the algorithm, the act ivity diagram of the r unning example in Figure 3 (b) is converted to the Petri net in Figure 4. The transformed Petri net is a bi-simulation of the activity diagram, which means they are semantically equal. So we can achieve the verification of the activity diagram by verifying the equivalent Petri net against same system properties. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 Authentication List 1. Convert an activity diagram into a Petri net Input: AD := an activity diagram Output: PN{P, T, A, M0} := a Petri net for each node N in AD if N is an initial node, final node, decision node, or merge node Generate a corresponding place in PN.P else // action node, fork node, or join node Generate a corresponding transition in PN.T for each edge E in AD N 1 := source node of E in AD N 2 := target node of E in AD M1 := corresponding place or transition of N1 in PN.P M 2 := corresponding place or transition of N 2 in PN.P if both N 1 and N 2 (initial nodes final node decision node merge node) Generate an auxiliary transition T 1 in PN.T Generate an arc start from M 1 to T 1 in PN.A Generate an arc start from T1 to M2 in PN.A else if both N 1 and N 2 (action node fork node join node) Generate an auxiliary place P 1 in PN.P Generate an arc start from M1 to P1 in PN.A Generate an arc start from P 1 to M 2 in PN.A else Generate an arc start from M1 to M2 in PN.A for each place without an incoming arc Generate an initial token for that place in PN.M 0 return PN Authorization Validation Fill Order Join Figure 4. The Petri net transformed from the order processing scenario B. Verifying Petri Nets Crosscutting concerns describe the r unning sequences between advices and primary behaviors in all paths of integrated models. These properties can be described in the form of Computation Tree Logic (CTL) formulas [13] naturally. CTL for mulas cannot be generated from aspect models by synthesizing conditions o f join points specified by pointcut models and checking the corresponding advice models appears at right places. This is because th at the context specified by a pointcut model would be changed after integration, and the join points matched by the pointcut model could no longer exist. In this approach, the properties to be checked are directly refined from crosscutting requirements. 1) Properties specified from the requirement Ship Order Close Order Fork Logging Informing Based on the Petri net generated, we can easily analysis reachability, safety, liveness, and fairness properties [8]. In this approach, we only focus on checking properties that are closely related to crosscutting concerns. We cat egorize crosscutting concerns from two facets. Firstly, according to the execution sequence between action in advice models and join points, a crosscutting concern can be eit her executing before or after join points. Secondly, the execution of a crosscutting concern is either sequential or parallel with the primary behaviors. Sequential crosscutting concerns are synchronous features that their running positions are restricted by the jo in points. Pa rallel crosscutting concerns are asynchronous features that are running concurrently with primary actions and they are finished or started by the join points. a) Before-crosscutting concerns A before-crosscutting concern specifies some extra behaviors must be performed before matched join points. Actions specified by a s equential before aspect model are executed between the j oin point node and the p redecessor node of the join point in the primary model. The key word of sequential before-crosscutting concerns in require ments level is “ before”. A parallel before aspect speci fies crosscutting actions that must be finished by the join point edge. The key word of parallel before-crosscutting concerns in requirements is “be finished by”. In the integrated model, the actions of the crosscutting concern are running concurrently with the pri mary behaviors, and then synchronized at the join node which replaced the join point edge. In corresponding Petri nets, assume jp is the transition transformed from one of the join point, ad is the transition transformed from the structured activity node that represents the advice model. The requirement of a before aspect can be represented in the f orm of the CTL formula as: AG (( ad EX( ad jp)) (( ad jp) EX jp)) . b) After-crosscutting concerns An after-crosscutting concern specifies some actions must be performed after matched join points. An aftercrosscutting concern can also be either a sequential or a parallel aspect with respect to the flows of primary models. Actions specified by a sequential after aspect model are the actions executed between the join point node and th e successor node of the join point in the primary model. The key word of sequen tial after-crosscutting concerns in requirements level is “after”. A parallel after aspect specifies crosscutting actions must be started by the join point edge. The ke y word of parallel after-crosscutting concerns in requirements is “be started by”. In the integrated model, the act ions of the crosscutting concer n are enabled by the f ork node, which replaced the join point edge, and then running concurrently with primary behaviors. In corresponding Petri nets, assume jp is t he net transition transformed from the join point, ad is t he net transition transformed from the structured activity node that represents the advice model. The requi rement of a sequential after aspect can be represented in the form of the 371
CTL formula as: AG (( jp EX( jp ad )) (( jp ad ) EX ad )) . 2) Conflicts of Multiple Crosscutting Concerns The CTL formula need to be adjusted if more than one crosscutting concerns (which are all “before” aspects or are all “after” aspects) match a sa me join point. Because t he running sequence between one aspect and a join point can be affected by other aspects of the same before/after kind, which match the same join point. For instance, in the running example, the authenti cation and authorization concerns are conflicted because they bot h are be forecrosscutting aspects and they have same join point, the “Fill_Order” action. The running sequence of authentication aspect and “Fill_Order” operation will be changed from “Authentication->Fill_Order” to “Authentication- >Authorization->Fill_Order” after the weaving of authorization aspect. a) Conflicts between two before-crosscutting concerns For a before-crosscutting concern cc 1 with advice model ad 1 and join point jp 1 , if any other before aspect, whi ch matches the same join point jp 1 and weaves after cc 1 , then some extra act ions are performed after ad 1 and b efore jp 1 . Assume it’s a bef ore-crosscutting concern cc 2 with advice ad 2 weaves after cc 1 , then jp 1 should be replaced by ad 2 in the CTL formula of cc 1 as: AG(( ad EX( ad ad )) 1 1 2 (( ad ad ) EX ad )). 1 2 2 b) Conflicts between two after-crosscutting concerns For an after-crosscutting concern cc 1 with advice ad 1 and join point jp 1 , if any other after aspect , which matches the same join p oint jp 1 and weaves after cc 1 , then some extra actions are performed after jp 1 and before ad 1 . Assume it’s an after-crosscutting concern cc 2 with advice ad 2 weaves after cc 1 , then jp 1 should be replaced by by ad 2 in the CTL formula of cc 1 as: AG(( ad EX( ad ad )) 2 2 1 (( ad ad ) EX ad )). 2 1 1 3) Verification After the system crosscutting properties are refined as a set of CTL formulas. We can veri fy the Petri net against specified CTL formulas generated. If the verification is passed, it means the model satisfies the corresponding crosscutting requirements. Otherwise, the model violates the corresponding crosscutting requirements to some extent, which means further revision about the model is needed. In the running example, the integrated model in Figure 1 (a) and (b) are both verified against the crosscutting requirements of authentication, authorization, validation, logging, and informing. First, the integrated models are transformed to Petri n ets. Then the 5 crosscutting requirements are refined to 5 CTL formulas. Finally, Petri net analyzer LoLA is employed to verify the two Petri nets against the formalized crosscutting requirements, respectively. The Petri net transformed from the model in Figure 3 (b) passes the verification process and output “result: true” for all the 5 C TL formulas. While the Petri net transformed from the model in Figure 3 (a) fails when verifying against the 2 CTL formulas generated from authentication and authorization requirements, and passes the verif ication against the other 3 CTL formulas. This verification result shows that the crosscutting requirements of authentication and authorization do not hold in this aspect-oriented model. After correcting the weaving preference fault and integrating the aspect model again, the new integrated model passes the verification process. C. Tool Implementation We implemented a tool named Jasmine-AOV 1 based on Topcased 2 and LoLA 3 . As Figure 5 shows, this tool is composed of 4 main parts: Model Transformer, Crosscutting Concern Manager, CTL Generator, and Model Checker. The Model Transformer converts an activity diagram to a Petri net automatically. The inputs of Model Transformer are UML diagrams designed by Topcased in the form of XML file and the outputs of the tool are Petri net files which are readable for LoLA to perform verification tasks. The Crosscutting Concern Manager is used to manage mapping relations between crosscutting concerns in requirements and elements in corresponding activity diagrams. It provides an assistant for mapping textual crosscutting requirements to design activity diagrams. The CT L Generator can automatically generate CTL f ormulas from crosscutting requirements that are mapped to d esign models. The CTL Generator also supports users to input CTL formulas manually. Model Checker is implemented by di rectly wrapped an existing checker, LoLA. It can ver ify the Petri net against crosscutting properties in the format of CTL formulas and report the result. Topcased Aspect-Oriented Models Crosscutting Requirements Model Transformer Crosscutting Concern Manager Crosscutting Concern in Design Models Jasmine-AOV CTL Generator Petri-Net CTL Formulas Figure 5. The framework of Jasmine-AOV LoLA Model Checker Verification Results The screenshot of Jasmine-AOV is in Figure 6. The “Crosscutting concerns” area manages the crosscutting requirements which are mapped to design models. The “New Crosscutting Concern” dialog provides an a ssistant for mapping textual crosscutting requirements to design activity diagrams. The “Petri net” area displays the Petri net transformed from the corresponding activity diagram. The “CTL Formulas” area lists t he formulas refined from crosscutting concerns in the “Crosscutting concerns” area automatically or wrote by users manually. The “Verification Results” area outputs the results of verifying the Petri net in 1 JasmineAOV,http://seg.nju.edu.cn/~zqcui/JasmineAOV 2 Topcased,http://www.topcased.org/ 3 LoLA,http://www.informatik.unirostock.de/tpp/lola/ 372
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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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Evaluating Open Source Reverse Engi
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Table 1. Candidate reverse engineer
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Some tools may perform the needed f
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Coordination Model to Support Visua
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this CMV approach we employee three
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colored according to the Gradient T
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Improving Program Comprehension in
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The method used to support and eval
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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
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An Approach for Software Component
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properties. In particular, the foll
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Conference Dataset Anatomy Dataset
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equipment maintenance node may cont
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Online Anomaly Detection for Compon
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B. Monitoring Static Method Invocat
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undle which is responsible for pars
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An Exploratory Study of One-Use and
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Figure 1. Mindmap of Reuse Design P
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subject also caused outliers for re
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Choosing licenses in free open sour
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The important aspect is to provide
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that holds a model of each software
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Online Probability Application Char
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TABLE II THE PARAMETERS FOR THE HAR
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[10] N. Gunther, “Hit-and-run tac
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Section 5 d iscusses some related w
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model and the analysis results. The
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tree view of UML model, as show in
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II. BACKGROUND In this section, we
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Algorithm 2 MarkStatements function
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(a) printtok (b) printtok2 (c) sche
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Gupta extended HGS and proposed the
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manage intellectual knowledge with
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D. Manage Training When any trainin
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TABLE II. COMPARISON BETWEEN THE EX
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B. Trace Semantics of DAP Fig. 1. A
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∀ [DR ′ ] []gen [; ] [D]□ [;
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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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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
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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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