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edges, which emanate from And and Or nodes, respectively. An unversioned product can be represented by an And/Or graph consisting exclusively of And nodes/edges. A versioned product is modeled by introducing Or nodes. Versioned objects and their versions are represented by Or nodes and And nodes, respectively. For example, Figure 1 illustrates an And/Or graph representing a base of objects. It is important to notice that finegrained visibilities of an object base can also be represented by And/Or graphs using the same formalisms discussed in this paper. However, we focused our examples on a coarse-grained visibility to allow a better understanding of the problem and our proposed solution. The main difference of coarse and fine granularity is the number of components and their versions, which enforces the necessity of faster algorithms to solve the problem when fine-grained components are in place. IV. SOLUTION TO THE OIV PROBLEM In this section we introduce a transformation of the OIV problem into a combinatorial problem related to And/Or graphs (MIN–AND/OR). Through this transformation we systematize a process where the versioning rules are converted into weights at the edges of the object base’s graph. Thus, we present an approach that enables the development of algorithms for solving the problem of intentional versioning. In addition, we present a backtracking algorithm for MIN– AND/OR which in turn solves the OIV problem. The MIN–AND/OR problem consists on finding a subgraph (solution subgraph) that matches a set of restrictions considering a weighted And/Or graph with a source vertex s. We introduce the definition of the MIN–AND/OR Problem[11] as follows: Problem: MIN–AND/OR Input: An acyclic And/Or graph G=(V,E) properly connected and weighted with a source vertex s ∈ V , where each vertex v possesses a label f(v) ∈{And, Or} and each edge possesses a weight τ(e) ≥ 1. Output: A subgraph H =(V ′ ,E ′ ) of G such as the sum of the weights of its arcs is minimum, and satisfies: 1) s ∈ V ′ ; 2) if v ∈ V ′ and f(v) =And then all out-arcs of v must belong to E ′ ; 3) if v ∈ V ′ and f(v) =Or then exactly one of the out-arcs of v must belong to E ′ . To transform the OIV Problem into the MIN–AND/OR Problem, it is necessary to weight the And/Or graph G (representing the object base) according to the query built from the versioning rules. A. Weighting the And/Or graph G Considering the And/Or graph G it is possible to add weights to its edges using two types of versioning rules: • Configuration Rules: a series of affirmative statements originated from questions asked to the stakeholders of the software; from these affirmatives, it is possible to exclude some versions from the space of possible versions. As an example of the formulation of the configuration rules, the configuration manager can ask how the user communicates with the system (local access, remote access). It becomes possible then to formulate the configuration rule: the software must provide local access support. This configuration rule discards the components that provide remote access or portability features, because they are not demanded. These rules consider the functional requirements of the product being generated, and can be replaced by an existing feature-based approach if the product line engineering process already has one in place. • Classification/Qualification rules: a set of items classified/qualified considering their priority to the stakeholders according to the needs and demands of the software. For instance, the stakeholders can ask for a product that allows high-end control capabilities and is efficient. The configuration manager then is able to formulate the Classification/Qualification rules: Control Capability, Efficiency. Versions of components that best excel at those two items are preferred. These rules consider the nonfunctional requirements of the product being generated. They are important to solve situations where there are open alternatives even after imposing the rules related to functional requirements. 1) Applying Configuration Rules: The set of configuration rules characterizes the first process of weighting the graph of the base of objects. The possibilities that do not accomplish the defined configuration rules are unable of existing in a solutionsubgraph with non-infinite cost. Along the weighting process, the configuration rule analysis dictates if an edge receives an infinite weight or not, sometimes excluding a large amount of versions (by excluding one component, all its versions are automatically excluded). The configuration rules are therefore defined as affirmative statements, originated from a set of questions answered in cooperation with the stakeholders. The software engineers should create questions with a high capability of constraining the version space. When applying configuration rules, the questions work as a way to formulate demanding affirmatives - the configuration rule itself. All edges of G pointing to vertices that oppose the affirmative receive infinite weight; all other vertices have their edges set with weight 1. According to this algorithm, a version with non-infinite weight is a version that matches the configuration rule not necessarily by accomplishing the configuration rule, but by not going directly against it. 2) Applying Classification/Qualification Rules: With the application of the classification/qualification rules, edge values bounded to each component will emerge. By doing so, some versions shall protrude, therefore distinguishing themselves from the others. The stakeholder must classify some criteria, according to his/her priority, aiming to reflect what is expected in the final version of the software. In other words, the 637
System Advanced Standard Module A Module B Module C Module D Version 1 Version 2 Version 1 Version 2 Version 1 Version 2 Version 3 Version 1 Version 2 Component 1 Component 2 Component 3 Component 4 Component 5 Component 6 Component 7 Component 8 Version 1 Version 1 Version 1 Version 1 Version 2 Version 1 Version 1 Version 1 Version 2 Version 1 Fig. 1. Example of a object base represented by an And/OR graph. stakeholder is demanded to prioritize non-functional aspects of the software. In this paper we named these criteria as Classification/Qualification rules. As an example of classification rules, the stakeholders can be asked to classify the following items according to their priority: Control Capability, Efficiency, and Support. It becomes possible, after this step, to choose the version that best matches the stakeholder’s priorities. The Classification/Qualification rules initiates by considering a set of criteria {s 1 , s 2 , s 3 ,...,s k }, in general, some of these criteria can be obtained from the ISO/IEC 9126 for the evaluation of software quality [2], such as: usability, efficiency, maintainability and portability. These criteria are classified by the stakeholder with the tags high relevancy, regular relevancy, low relevancy. Next, the software engineer classifies each component of the object base according to the quality (bad, regular, good, excellent or does not interfere/ not relevant) of each s j criterion. After that, it is proposed a weighting process of the And/Or graph as follows: To each component c i do: 1) Add • weight 0 to the criterion classified as excellent or not relevant; • weight 1 to the criterion classified as good; • weight 2 to the criterion classified as regular; • weight 3 to the criterion classified as bad. 2) Calculate • the sum of the weights of the high relevancy criteria as HR; • the sum of the weights of the regular relevancy criteria as RR; • the sum of the weights of the low relevancy criteria as LR; 3) Sum W to the weight of the vertex in-edge that represents the component c i (W =3HR +1.5RR + LR). The W value is calculated to ensure that the weight of a high relevancy criterion is the double of the regular relevancy criterion weight and the triple of the low relevancy criterion weight. Consequently, a high relevancy criterion classified/qualified as good corresponds to a regular relevancy criterion classified as regular or to a low relevancy criterion classified as bad. Clearly the higher relevancy criteria weights more, what is justified because of the MIN-AND/OR problem structure. The MIN- AND/OR Problem consists on the weight minimization in which the higher relevance criteria weight is expected to be the smallest. Figure 2 illustrates the object base shown in Figure 1 after applying a possible set of versioning rules. At this point it is important to highlight that other possible versioning, with different versioning rules, would produce another weighting of the G graph; however, as the object base remains the same, the graph itself is not rebuilt. In addition, even when there are changes on the object base, the G graph is capable of easy adapting and still has not to be rebuilt. With the And/Or graph properly weighted, it is possible to state that: 1) a version of the software corresponds to a subgraph solution-subgraph) of G, such as: • The source vertex (representing the software) belongs to solution-subgraph. • If a And-type vertex belongs to the solutionsubgraph then all its out-edges do as well. • If an Or-type vertex belongs to the solutionsubgraph then exactly one of its out-edges does as well. This observation is easily verifiable. The out-edges of an And vertex represents composition, that is, if a module belongs to a version z so do all of its components. On the other hand, out-edges of an Or vertex represents version options of a component; as a software uses at most one version of each component, this observation is true. 2) To find the optimal intensional version of the OIV Problem corresponds to find the solution-subgraph with minimum cost of the G graph. In our example, the 638
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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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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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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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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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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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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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Page 631 and 632: USE SCENARIO The application Vuelos
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Page 637 and 638: B. Component Model Fig. 2 show s th
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Page 651 and 652: Empirical Validation of Variability
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Page 711 and 712: Once the datasets were evaluated us
Page 713 and 714: Reconfiguration of Robot Applicatio
Page 715 and 716: data dependencies required for keep
Page 717 and 718:
Spacemaker: Practical Formal Synthe
Page 719 and 720:
Fig. 1. The ecommerce object model.
Page 721 and 722:
Fig. 3. Mapping diagram for Figure
Page 723 and 724:
A formal support for incremental be
Page 725 and 726:
machine may be empty which should l
Page 727 and 728:
software, based on revised requirem
Page 729 and 730:
A Process-Based Approach to Improvi
Page 731 and 732:
Appropriate processes m ust support
Page 733 and 734:
Figure 2. Reordered layers of the k
Page 735 and 736:
Automatic Acquisition of isA Relati
Page 737 and 738:
III. VALIDATING THE DIMENSION HEADE
Page 739 and 740:
The table title and the dimension s
Page 741 and 742:
A light weight alternative for OLAP
Page 743 and 744:
i.d. subsets of cubes focused on se
Page 745 and 746:
Match production rules Enterprise S
Page 747 and 748:
A Tool for Visualization of a Knowl
Page 749 and 750:
other ontology visualization tools
Page 751 and 752:
shown at Figure 5. Objectives are s
Page 753 and 754:
Rendering UML Activity Diagrams as
Page 755 and 756:
a = O1 → a → O2 b = O2 → b
Page 757 and 758:
ecordProblem → A → reproducePro
Page 759 and 760:
umlTUowl - A Both Generic and Vendo
Page 761 and 762:
The tool’s ability to deal with S
Page 763 and 764:
Elem. UML Example D 12 Harmonizing
Page 765 and 766:
4) Associations: In the first test
Page 767 and 768:
III. GRAPH REPRESENTATION OF CLASS
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as an add-in to popular UML m odeli
Page 771 and 772:
742
Page 773 and 774:
744
Page 775 and 776:
746
Page 777 and 778:
her necessities. However, the progr
Page 779 and 780:
Figure 3. Global Log. of terms. The
Page 781 and 782:
two stages. The first stage is focu
Page 783 and 784:
754
Page 785 and 786:
756
Page 787 and 788:
758
Page 789 and 790:
With the GDUC approach, we can mode
Page 791 and 792:
Figure 6. Three proposals containin
Page 793 and 794:
764
Page 795 and 796:
766
Page 797 and 798:
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
Page 811 and 812:
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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