SEKE 2012 Proceedings - Knowledge Systems Institute

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) Gazetteer: This component creates annotation to offer information about entities (persons, organizations, etc.) using lookup lists. c) POS tagger: This component produces tags to words or symbols. d) Sentence splitter: This component identifies and annotates the beginning and the end of each sentence. e) NE transducer: This component applies JAPE (Java Annotations Pattern Engine) rules [29] to input text in order to generate new annotations. f) Semantic tagger: This component contains rules which act on annotations assigned earlier, in order to produce outputs of annotated entities. 2) Semantic unification: This unification level concerns the content of patterns’ labels. It co nsists in a te xt mining process, extracting terms and / or concepts that are most representative for the different patterns fields’ content. These latter (terms, concepts) are weighted according to their occurrence number in the content and then, sorted according to their weight representing thus, a sem antic annotation for the concerned features. The patterns’ fields might also be enriched by synonyms extracted from the WordNet [30] ontology for the English as well as the WolF [31] ontology for the French language, contributing thus, to th e enrichment of these semantic annotations. The semantic unification phase is performed using the GATE framework on structured patterns produced by the first phase. It adds semantic annotations to patterns content to form what we have called semantic process patterns (c.f. Fig. 7). 3) Architectural unification: This phase aims to normalize the description of an y given process pattern by converting it from XML to OWL (Ontology Web Language). In other words, this unification level should allow the autmatic population of the proposed OWL ontology, named MetaProPOS with individuals based on a given patterns corpus. In order to ach ieve this, we use the Jena framework [32] to implement a Java component for ontology’s population based on the produced semantic process pattern s of the two previous unification phases as well as some extraction rules established for this purpose (c.f. Fig. 8). For inf ormation purpose, Jena is a Java framework for building Semantic Web applications. It provides a collection of tools and Java libraries as well as an ontology’s API for handling OWL ontologies in order to help develop semantic web applications [32]. B. Patterns Mining Process Since the overall goal of our research work is to build up a semantic and intelligent framework enhancing software process patterns capitalization and reuse, we adopted a process patterns warehousing and mining technique. As mentioned earlier, the pattern warehousing process is ensured by the three unification levels detailed above. As regards the pattern mining process, it consists on a reasoning process performed on the unified patterns in the warehouse based on MetaProPOS and the key ontolgy’s concept Relationship. Indeed, this main concept would allow a better search of related patterns. Unstructured patterns Format: Pdf, Html, Doc, Txt Form: PPL, UML-PP, Ambler, etc. ANNIE’s Processing Ressources Process patterns OWL ontology’s Jena API Semantic Process patterns 1. Morphological Analysis Extended ANNIE NE Transducer Gazetteer Lists JAPE Rules 2. Key Terms Recognition Figure 6. Terminological unification. Semantic Unification Figure 7. Semantic unification. Architectural Unification Format : XML Form : unified Figure 8. Architectural unification. WordNet WolF MetaProPOS ANNIE Sentence Splitter 3. Pattern Tagging Tokeniser POS Tagger Semantic Tagger Ontology’s instances Annotations Structured patterns Semantic process patterns 509
These latter would be clustered according to the relationship kind (c.f. Fig. 4). Consequently, a patterns’ algebra is created allowing rigorous and automatic patterns processing providing better search of similar patterns through the Similar and Alternative relations as well as efficient guidance for patterns composition and aggregation through the Refinement, Consequent and Anti-pattern relations. Furthermore, process patterns could be clustered according to the software development phase or activity. So, a mega process pattern could be built as an aggregation and / or a composition of different process patterns for a given context or problem. In order to reach this objective, we propose to combine the use of MetaProPOS with a mining tool such as Weka [33] which consists in a collection of machine learning algorithms for data mining tasks containing tools for data pre-processing, classification, regression, clustering, association rules, and visualization. V. CONCLUSION AND WORK IN PROGRESS The most valuable contribution of this paper is the general overview of targeted approach SCATTER which aims to provide a s emantic framework for process patterns warehousing and m ining given different patterns collections. This research work mediation implies terminological, semantic as well as architectural mediation efforts ensured by the three proposed unification levels. The process patterns’ mining process would improve process pattern’s capitalization and reuse quality. However, SCATTER has not been completely finalized for implementation and is subject to refinement and validation, which remains our work in progress. Indeed, we should expand the proposed approach to annotate figures described through Petri nets to cover workflow process patterns [34] as well as UML activity diagrams. In this direction, we plan to combine the use of the UIMA plug-in and ANNIE to deal with image analysis and structuring in addition to text since GATE does not support this kind of information. REFERENCES [1] Buschmann, F., Henney, K., and Schmidt, D.C., “Pattern-oriented Software Architecture: On Patterns and Pattern Languages”, Wiley & Sons, 2007. [2] Henninger, S., Corrêa, V., “Software pattern communities: current practices and challenges”, 14th International Conference on Pattern Languages of Pr ogramming, pp. 1--19. ACM Proceedings, New York, 2007. [3] Conte, A., Fredj, M., Giraudin J.P., and Rieu, D., “P-Sigma: a formalism for A unified representation of patterns (in French), 19ème Congrès Informatique des Organisations et Systèmes d'Information et de Décision, pp. 67--86. Martigny, 2001. [4] Hagen, M., “Support for t he definition and usage of p rocess patterns”, 7th European Conference on Pattern Languages of Programs, Dortmund, 2002. [5] Hagen, M., and Gruhn, V., “Process patterns - a m eans to describe processes in a flexible way”, 5th International Workshop on Software Process Simulation and Modeling, ICSE Workshops, pp. 32--39. Scotland, 2004. [6] Tran, H.N., Coulette, B., and Dong, B.T., “Modeling process patterns and their application”, 2nd International Conference on Software Engineering Advances, pp. 15--20, IEEE Proceedings, Cap Esterel, 2007. [7] Tasharofi, S., and Raman, R., “Process patterns for agile methodologies”, Situational Method Engineering: Fundamentals and Experiences, Proceedings of the IFIP WG 8.1 Working Conference, pp. 222--237,Springer, Switzeland, 2007. [8] Ambler, S.W.: Process Patterns: Building Large-Scale Systems Using Object Technology. Cambridge University Press/SIGS Books, Cambridge, 1998. [9] Kouroshfar, E., Yaghoubi Shahir, H., and Ramsin, R., “Process patterns for component-based software development”, CBSE 2009, LNCS 5582, pp. 54--68, 2009. [10] Fahmideh, M., Sharifi, M., Jamshidi, P., Feridoon, S., and Haghighi, H., "Process patterns for service-oriented software development", proceedings of the 5th IEEE International Conference on Research Challenges in Information Science (RCIS’2011), pp. 1--9, 2011. [11] Khaari, M., and Ramsin, R., “Process patterns for aspect-oriented software development”, ECBS, pp. 241--250, England, 2010. [12] Jlaiel, N., and Ben Ahmed, M., “MetaProPOS: a meta-process patterns ontology for soft ware development communities”, KES Proceedings, Part I. LNCS 6881, pp. 516--527 Springer, Germany, 2011. [13] Jlaiel, N., and Ben Ahmed, M., “Ontology and ag ent based model for software development best practices’ integration in a knowledge management system”, OTM Workshops, OntoContent 2006, LNCS 4278, pp. 1028 -- 1037, Springer, France, 2006. [14] Gzara, Yesilbas, L., Rieu, D., and Tollenaere, M., “Patterns approach to product information systems engineering”, Requirement. Engineering, vol. 5(3), pp. 157--179, 2000. [15] Bouassida, N., and Ben-Abdallah, H., “A new approach for pattern problem detection”, CAiSE, LNCS, Tunisia, pp.150--164, 2010. [16] Störrle, H.,”Describing process patterns with UML”, 8th EWSPT, LNCS, vol. 2077, pp. 173--18, Springer, Witten, 2001. [17] Dittmann, T., Gruhn, V., Hagen, M.: Improved Support for the Description and Usage of Process Patterns. In: 1st Workshop on Process Patterns, 17th ACM Conference on Object-Oriented Programming, Systems, Languages and Applications, pp. 37--48. Seattle, 2002. [18] Gnatz, M., Marschall, M., Popp, G., Rausch, A., and Schwerin, W.: “Towards a tool support for a living software development process”, 8th EWSPT 2001. LNCS, vol. 2077, pp. 182--202. Springer, Witten, 2001. [19] Coulette, B., Crégut, X., Dong, T.B., Tran, D.T., “RHODES, a process component centered software engineering environment”, 2nd International Conference on Enterprise Information Systems, pp. 253-- 260, Stafford, 2000. [20] Ribó, J.M., and Franch X., “Supporting Process Reuse in PROMENADE”, Research report, Politechnical University of Catalonia, 2002. [21] Hagen, M., and Gruhn, V., “Towards flexible software processes by using process patterns”, 3rd IASTED Conference on Software Engineering and Applications, pp. 436--441, Cambridge, 2004. [22] http://www.cs.kent.ac.uk/people/staff/saf/patterns/diethelm/plmlx_doc [23] Meng, X.X., Wang, Y.S., Shi, L., and Wang, F.J., “A process pattern language for agile methods”, 14th Asia-Pacific Software Engineering Conference, pp. 374--381, Nagoya, 2007. [24] Jlaiel, N., and Ben Ahmed, M.,”Reflections on how to improve software process patterns capitalization and reuse”, IKE, pp 30--35, USA, 2010. [25] http://opennlp.apache.org/ [26] http://uima.apache.org/ [27] http://gate.ac.uk/ [28] http://gate.ac.uk/sale/tao/splitch6.html#chap:annie [29] http://gate.ac.uk/sale/tao/splitch8.html#chap:jape [30] http://wordnet.princeton.edu/ [31] http://alpage.inria.fr/~sagot/wolf-en.html [32] http://incubator.apache.org/jena/ [33] http://www.cs.waikato.ac.nz/ml/weka/ [34] Van der Aalst, W.M.P., Ter Hofstede, A. H.M., Kiepuszewski, B., and Barros, A.P., “Worflow patterns”, Distributed and parallel Databases, 14(3), pp. 5--51, 2003. 510
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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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320
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A model introducing SOAs quality at
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there exist a hierarchical ranking
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Software as a Service: Undo Hernán
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operations consist in reinjection i
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stored. If event failure, external
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ensure users the trustworthiness of
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Patient Doctor Doctor Patient Direc
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going down at a time. This is a rea
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Decidability of Minimal Supports of
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A S 1 1 1 0 0 0 1 1 0 0 0 1 1
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satisfies R( D) R( C) S 1. Compa
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Automated Generation of Concurrent
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target of testing. A blackbox test
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sequence. Each t i θ i in the firi
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SAMAT -AToolforSoftware Architectur
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Figure 4. The SAM Hierarchical Mode
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High-level Petri net Place Place Ty
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verification and thus is often limi
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(r 1 r 2 ...r k ) 1 denotes Path [r
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Fig. 3. Example a 3-compound weakly
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364
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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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Page 493 and 494: Testing Interoperability Security P
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Page 515 and 516: A Tiny Specification Metalanguage W
Page 517 and 518: C. Syntax Extensions The expressive
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Page 525 and 526: Figure 1. The process for engineeri
Page 527 and 528: Figure 4. The abstractions extracte
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Page 531 and 532: Algorithm 1: Algorithm to recursive
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Page 535 and 536: A. Pattern Reuse In patterns reuse
Page 537: IV. SCATTER In order to enhance pat
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Page 543 and 544: DC2AP Element and their Application
Page 545 and 546: 21. Consequences Example of use DC2
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Page 549 and 550: 2.3.1 Complementarity of MDD techno
Page 551 and 552: 3.2.2 EAggregateRelationship The EA
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Page 555 and 556: II. OVERVIEW OF MODAL TRANSITION SY
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Page 559 and 560: Suppose P = 〈S P , SP i , AI P ,
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Page 563 and 564: A. Step S1 - Define the Business Pr
Page 565 and 566: F. Step S6 - Monitor at Run-Time Th
Page 567 and 568: Figure 1, a metamodel defines an XM
Page 569 and 570: Figure 3 is a feature diagram that
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Page 573 and 574: Pub-T Tc Tc Sub-T Sub-T Figu
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Page 579 and 580: proposed in this work requires the
Page 581 and 582: FTS is an important research area,
Page 583 and 584: C. Dependent Variables and Measures
Page 585 and 586: D. Conclusion Validity Conclusion v
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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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