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Bridging KDM and ASTM for Model-Driven Software Modernization Gaëtan Deltombe Netfective Technology Software 32, avenue Léonard de Vinci 33600 – Pessac, France g.deltombe@netfective.com Olivier Le Goaer, Franck Barbier University of Pau Avenue de l’université 64000 – Pau, France {olivier.legoaer, franck.barbier}@univ-pau.fr Abstract Standardizing software modernization techniques has lead to the KDM (<strong>Knowledge</strong> Discovery Metamodel). This metamodel represents several application aspects (code, architecture, etc.), while transforming them into renewed versions. On the other hand, ASTM (the Abstract Syntax Tree Metamodel) has been recently released. It focuses on the parsing of text-based files written in a given language. In practice, KDM and ASTM are intended to be used jointly when modeling source code with formal links to other software features like components, user interfaces, etc. However, the link between ASTM and KDM is often fuzzy, or even unestablished since KDM is in charge of synthesizing all captured software artifacts. This has negative effects on the attainable level of automation and on the completeness of a software modernization project. To overcome this limitation, this paper introduces SMARTBRIDGE as a means to reconcile both standards. 1. Introduction Modernization is at the heart of many software organizations that seek to migrate from obsolete or aging languages and platforms to more modern environments. Modernization projects have historically focused on transforming technical architectures; that is, moving from one platform to another and / or from one language to another [2]. This is achieved through code translation or various refactoring exercises such as restructuring, data definition rationalization, re-modularization or user interface replacement. Meanwhile, model-driven development (MDD) is gaining increasing acceptance; mainly because it raises the level of abstraction and automation in software construction. MDD techniques, such as metamodeling and model transformation, not only apply to the creation of new software systems but also can be used to help existing systems evolve [7, 1]. These techniques can help reduce software evolution costs by automating many basic activities, including code manipulation. There is currently great activity addressing model-driven modernization issues, for which the OMG’s task force on modernization [16] plays a major role. It aims at making precise inventories of various kinds of legacy artifacts in order to propose more or less automatic model-driven migrations of applications by means of interoperable tools. This way, all discovered artifacts are considered as full-fledged models. This is an important shift compared to classical approaches, which do not consider abstract representations as perennial and reusable assets. The context of this paper is rooted in the research work conducted by the European REMICS (www.remics.eu) project [11]. This project seeks an end-to-end model processing chain to transform legacy applications/information systems into services in the Cloud. Several modeling languages are operated in the project, within both reverse and forward engineering activities. To define and support this chain in tools, modeling language semantic gaps must be fulfilled. More specifically, the reverse engineering activity must be committed to go from the source code towards technologically-neutral UML models, which can then be consumed by a wide range of third-party MDD tools. The forward engineering activity starts from UML models and next uses SoaML (www.soaml.org) and CloudML (a forthcoming OMG standard that is currently specified within REMICS). So, between reverse and forward, a suite of models conforming to (i.e., instances of) OMG standard metamodels are involved, each aiming at representing the initial source code at different levels of abstraction, along with various refinements/quality levels [8]. The general idea is that a portfolio of metamodels and transformation chains are pre-implemented in a tool to support an intelligible seamless reverse and forward engineering process. Because of its industrial impact, REMICS complies to worldwide standards. Nonetheless, these standards lack large-scale experimentation, thus requiring adaptations in their core to really achieve a high degree of automation when targeting a 517
Legacy2Cloud logic. In this scope, this paper stresses reverse engineering activity, by showing and illustrating how modeling language can be rationally treated. In reverse engineering, top-down approaches promote a recovery process that is conducted through architectual knowledge. On the other hand, bottom-up approaches [4] are more pragmatic, since reverse engineering activities lean on source code that is undoubtedly the most rich, self-contained and straightforwardly available material. REMICS focuses on the latter due to the dispersion, or even absence of knowledge. So, model transformations amount to inferring such knowledge from model details and from the expressiveness of their associated modeling languages. Risks occur when the knowledge in models has to move from one language formalism to another. For turning code written in a given programming language into a language-agnostic model, the OMG’s task force on modernization puts forward two metamodels: the ASTM [13], which is a metamodel dedicated to syntax trees, and the KDM [12], which is a more global metamodel where the sub-parts deal with a wider spectrum of program-level elements (i.e., user interfaces, data, functions/services, running platform). Unfortunately, KDM and ASTM are not clearly linked to each other. They aim at being complementary but practice shows an unsound, ill-formalized dependency between them. To bridge this gap, this paper discusses, develops and illustrates an intermediary metamodel called SMARTBRIDGE, which allows a roundtrip relationship between KDM and ASTM and hence supports multiple iterations during the reverse engineering activity. The remainder of the paper is organized as follows: in Section 2 we describe the motivations and the various specifications that gave birth to KDM and ASTM respectively. The SMARTBRIDGE is then detailed in Section 3, including an enumeration of the metaclasses and metarelationships involved in the junction of ASTM and KDM. To make these ideas more concrete, we provide in Section 4 a demonstration of SMARTBRIDGE on a tiny COBOL snippet. Section 5 gives an overview of the related works on model-driven modernization. We conclude this paper in Section 6. 2. Problem of Interest Recently, MDD standardization has stressed modernization with the special objective of providing new concepts, tools and processes when moving legacy software to renewed applications/information systems running on top of the most up-to-date technologies. The idea behind that is to switch between different technical spaces [10] by underestimating the purely ”grammarware” approach in favor of the ”modelware” approach. Beyond traditional code-to-code approaches, model-centric approaches are those promoted by MDD in general: (a) conformance to well-defined meta- Application Src code Model (conforms to a metamodel) SASTM model ASTM model GASTM model P T T P Parser (injector/discoverer) T KDM model Transformation (Model-to-model ) Figure 1. Reverse Engineering process according to the OMG’s task force on modernization. models, (b) powerful transformation techniques, (c) easier integration of various concerns. Standards in this area also emphasize wider interoperability. In this case, standardcompliant models should be exchangeable between tools involved in the migration chain. These tools process software elements having different shapes. These shapes include the lowest levels (code), as well as the highest: business rules process extraction and interpretation, dealing with the software architecture and components, exhibiting services (business functionalities), etc. 2.1. MDD-based Reverse Engineering A modernization process encompasses two stages: reverse engineering and forward engineering. The forward engineering stage starts from a Platform-Independent Model (PIM) that serves as the basis for code generation. The reverse engineering stage extracts elements from legacy code and data description, rendering them into a Platform- Specific Model (PSM). KDM is the support candidate for representing PSMs by using ASTM as sub-support for the precise and comprehensive representation of a software system. The creation of PIMs (or technology-neutral models) is favored by the construction of formal mappings between KDM/ASTM on one side and UML (for PIMs) on the other side. More generally, metamodeling fosters the description of discrete steps to show how, at the very beginning, the rough code may be interpreted and analyzed in terms of architectural incidence: operating system adherences, business value discovery strategies, etc. In this article, KDM is the pivot metamodeling language for representing entire enterprise software systems; including source code of course, but not exclusively. As a common intermediate representation for existing software systems, KDM is a good support for refactoring, the derivation of metrics and the definition of specific viewpoints. Figure 1 depicts the full reverse engineering process promoted by MDD modernization standards. 518
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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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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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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
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
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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
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umlTUowl - A Both Generic and Vendo
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The tool’s ability to deal with S
Page 763 and 764:
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
Page 781 and 782:
two stages. The first stage is focu
Page 783 and 784:
754
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756
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758
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With the GDUC approach, we can mode
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Figure 6. Three proposals containin
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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