SEKE 2012 Proceedings - Knowledge Systems Institute

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Author’s Index A Sudipta Acharya, 51 Frank José Affonso, 668 Rui L. Aguiar, 387 Moataz A. Ahmed, 737 Alaeddin M.H Alawawdeh, 747 Adriano Albuquerque, 574 Antonio Juarez Alencar, 319 Gary Allen, 418 Norah Alrayes, 706 Tatiane O. M. Alves, 491 Jiufang An, 340 César Andrés, 464, 747 Reghu Anguswamy, 194 Nicolas Anquetil, 118 Toshiaki Aoki, 672 Eduardo Aranha, 557 Wandresson Araújo, 545 Wagner Arbex, 491 Hazeline U. Asuncion, 412 Jorge L. N. Audy, 551 Werney Ayala, 273 A. Azadmanesh, 305 B Rosa M. Badia, 88 Ebrahim Bagheri, 663 Hamid Bagheri, 688 Yunxia Bao, 340 Franck Barbier, 517 Moncef Bari, 406 Kamel Barkaoui, 375 Fabiane Barreto Vavassori Benitti, 143 Riad Belkhatir, 324 Mohamed BEN AHMED, 505 Swapan Bhattacharya, 315 Muhammad U. Bhatti, 118 J. M. Bieman, 763 Sandra Rodrigues Sarro Boarati, 594 Bruno Bonifácio, 588 Hanifa Boucheneb, 375 José Luís Braga, 511 Regina M. M. Braga, 491 Jacques D. Brancher, 225 Kellyton dos Santos Brito, 311 Ricardo Britto, 273 Lei Bu, 369 Frederico Moreira Bublitz, 426 C Lizhi Cai, 279 Zining Cao, 525 Miriam A. M. Capretz, 432 Fernanda Campos, 491 Jackson Casimiro, 712 Jaelson Castro, 444, 448, 531, 651 Ana Cavalli, 464 Christine W. Chan, 718 Debasis Chanda, 315 Shi-Kuo Chang, 1, 180 D.Y. Chao, 359 Laura Maria Chaves, 80 Daoxu Chen, 217 Jiaxi Chen, 211 Qiaoqiao Chen, 231, 235, 239 Xiangping Chen, 211 Xiaohong Chen, 61 Zhenyu Chen, 139, 267, 470 Zhong Chen, 617 Xiang Chen, 217 Francesco Colace, 180 Xabriel J. Collazo-Mojica, 88 Daniel B. F. Conrado, 495 Tayana Conte, 33, 582, 588, 657 Jonathan Cook, 154 Hugo Cordeiro, 712 Alexandre Luis Correa, 319 Ronaldo C. M. Correia, 168 Leandro T. Costa, 112 Sérgio Roberto Costa Vieira, 33 Anne-Lise Courbis, 694 Jonathas Cruz, 273 Zhanqi Cui, 369 Rafael Cunha, 657 Sean Curley, 400 Diego M. Curtino, 599 Ricardo M. Czekster, 112 D Álvaro F. d’Arce, 168 Daniel Alencar da Costa, 557 Lucas Francisco da Matta Vegi, 511 Alberto Rodrigues da Silva, 66 Glauber Luis da Silva Costa, 511 A-6
Maicon B. da Silveira, 112 Aldo Dagnino, 458 Filipe Bianchi Damiani, 605 José Renato Villela Dantas, 80 Eduardo Santana de Almeida, 628, 641, 657 Hyggo Oliveira de Almeida, 426 Vanilson André de Arruda Burégio, 311 Francisco Tiago Machado de Avelar, 438 Rodolfo M. de Barros, 225 Valter V. de Camargo, 495 Cedric L. de Carvalho, 256 Agustín De la Rosa H., 599 Silvio Romero de Lemos Meira, 311, 628, 641 Flávio M. de Oliveira, 112 Max Gontijo de Oliveira, 256 Massimo De Santo, 180 Abraham L. R. de Sousa, 570 Adler Diniz de Souza, 753 Gaëtan Deltombe, 517 Yong Deng, 617 Diego Dermeval, 444, 448, 651 Prem Devanbu, xxv Oscar Dieste, 328 Junhua Ding, 363 Weimin Ding, 768 Zuohua Ding, 135 Rogério do Nascimento, 33 Anderson R. P. Domingues, 647 Derek Doran, 400 Uéverton dos Santos Souza, 635 Weichang Du, 663 Xingzhong Du, 139 Leonardo Simas Duarte, 158 Stéphane Ducasse, 118 Animesh Dutta, 51 Haimonti Dutta, 100 Geycy Dyany, 174 E Armin Eberlein, 261 Magdalini Eirinaki, 13 L. Eisen, 763 Jorge Ejarque, 88 Shimaa M. El-Sherif, 261 Danilo M. Eler, 168 Matthew Engskow, 39 Sergio España, 531 F Behrouz Far, 261 Behrouz H. Far, 70 Stefan Farfeleder, 19 Pedro Porfírio Muniz Farias, 80 Paulo H. L. Fernandes, 551 Priscila Fernandes, 588 David de Almeida Ferreira, 66 Anthony Finkelstein, 448 Daniel F. Fitch, 333 Ioannis E. Foukarakis, 200 William B. Frakes, 194 Marília Aranha Freire, 557 Yoshiaki Fukazawa, 480 G Irbis Gallegos, 678 Cui Gang, 131 Kehan Gao, 74 Xin Gao, 541 Rogério E. Garcia, 168 Vinicius Cardoso Garcia, 311 Raúl García-Castro, 611 Ramón Garcia-Martínez, 25, 328 J. Garcia-Rejon, 763 Wander Gaspar, 491 Ann Gates, 678 Tamer Fares Gayed, 406 Qiang Ge, 422 S. Ghosh, 763 Itana M. S. Gimenes, 622, 647 Rafael A. Glanzner, 551 Swapna S. Gokhale, 162, 400, xxvii Hassan Gomaa, 394 Edson S. Gomi, 563 Éric Grégoire, 243, xxvii Katarina Grolinger, 432 Andreas Grünwald, 730 Qing Gu, 217 Yulong Gu, 279 Gabriela Guedes, 444, 651 Simon Suigen Guo, 718 H Hao Han, 124 Dan Hao, 283 Xudong He, 352 A-7
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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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322
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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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366
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368
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necessary before verification. In t
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CTL formula as: AG (( jp EX( jp ad
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equirements level, like EA-Miner [1
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376
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378
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380
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II. RESOURCE-ORIENTED WORKFLOW MODE
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eventually triggers a co mmon task.
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Input: A resource oriented workflow
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access control exists, the action i
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(ACCDA), is customized to address a
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ICrudSchema and shown in Figure 3.
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Connectors for Secure Software Arch
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Non-repudiation security service pr
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anAsynchronousCustomer InterfaceCon
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How Social Network APIs Have Ended
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OSN Social Feed Unique Features Con
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users can control the reach of thei
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Computer Forensics: Toward the Cons
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In 2009, Cohen et al. in [4] have o
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dcterms:contributor, dcterms:creato
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A Holistic Approach to Software Tra
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Figure 1. Figure 1 shows an overvie
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B. Case study in a proprietary proj
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Pointcut Design with AODL Saqib iqb
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system attributes, methods and exec
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Feature modeling and Verification b
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hasOptionalPart hasPart hasMandato
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A Context Ontology Model for Pervas
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Figure 1. UML view of the model. Th
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Figure 3. Architecture for deliveri
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Ontology-based Representation of Si
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Figure 2. Upper Ontology fragment f
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I2Sim simulation model was transfor
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An ontology-based approach for stor
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fying the equivalences between conc
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• Homonymy conflicts: occur when
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Automatic Generation of Architectur
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we dealt w ith the verticals rules.
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Towards Architectural Evolution thr
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different from replacing it, we dec
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Using FCA-based Change Impact Analy
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Table 2. Impact set of C1,C2,C5 cha
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Figure 3. The PF and PTS results fo
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Forecasting Fault Events in Power D
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which the airport data did not cont
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classified. Recall is the probabili
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Testing Interoperability Security P
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Figure 2. Overview of the Test Gene
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Objective Figure 4. Testing
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A New Approach to Evaluate Path Fea
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k v ( df i ) 0 , it means that the
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Model & Method Feasibility Evaluati
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Structural Testing for Multithreade
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adequate to another criterion. This
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480
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482
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484
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A Tiny Specification Metalanguage W
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C. Syntax Extensions The expressive
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however, does not allow variables f
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derivation dependencies. The third
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model extension as proposed in [6].
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Figure 1. The process for engineeri
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Figure 4. The abstractions extracte
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way to detect something which could
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Algorithm 1: Algorithm to recursive
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obtain the input of experts on desi
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A. Pattern Reuse In patterns reuse
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IV. SCATTER In order to enhance pat
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These latter would be clustered acc
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described or even r etrieved, thus
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DC2AP Element and their Application
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21. Consequences Example of use DC2
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Legacy2Cloud logic. In this scope,
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2.3.1 Complementarity of MDD techno
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3.2.2 EAggregateRelationship The EA
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cess. The proposal follows the ADM
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II. OVERVIEW OF MODAL TRANSITION SY
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(1) V □ (A) ⊆ V □ (B), V ♦
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Suppose P = 〈S P , SP i , AI P ,
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Adaptive software should basically
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A. Step S1 - Define the Business Pr
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F. Step S6 - Monitor at Run-Time Th
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Figure 1, a metamodel defines an XM
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Figure 3 is a feature diagram that
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ecause the system of C-net model is
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Pub-T Tc Tc Sub-T Sub-T Figu
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Visual Studio Achievements, a Visua
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the project. This achievement has t
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proposed in this work requires the
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FTS is an important research area,
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C. Dependent Variables and Measures
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D. Conclusion Validity Conclusion v
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evaluation of team productivity, qu
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Figure 3: Process Fragment Example
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complexity issues traditionally inv
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Fig. 1: Swing Framework Class Diagr
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ize:Class”, with an empty precond
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Fig. 11: Overwritten Method Fig. 12
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Investigating the use of Bayesian n
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process for the network. Thus, this
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Reuse of Experiences Applied to Req
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Step 1: To start the process, the u
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Specification of Safety Critical Sy
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current belief of agents in order t
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Using the Results from a Systematic
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obtained from the categorization of
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that automatically presents the usa
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Improving a Web Usability Inspectio
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Following the experimental methodol
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About the category “Improvements
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Identification Guidelines for the D
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created according to the preview re
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In our analysis, the “Government
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In the following, we present the re
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USE SCENARIO The application Vuelos
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[8] Mayhew, D., “The Usability En
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Interaction State Set Structure Dat
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B. Component Model Fig. 2 show s th
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As a continuation of our research w
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PROMETHEE methods [3] belong to a f
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comparison rule, a value of 1/9 is
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TABLE V: Supermatrix for the exampl
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for knowledge sharing. Based on str
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y applying hash functions to its su
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Empirical Validation of Variability
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III. EXPERIMENTAL STUDY In this sec
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Table III SPEARMAN’S CORRELATION
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A Mapping Study on Software Product
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most relevant sources in software e
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TABLE IV TOOL’S CLASSIFICATION AC
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[2] P. Clements and L. Northrop, So
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and future works. II. BACKGROUND ON
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System Advanced Standard Module A M
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Algorithm 1: Backtracking for MIN-A
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outcomes from such research fields,
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TABLE IV. CONTINUED FROM PREVIOUS P
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contribution of each study was made
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assets, the plugin approach can be
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Figure 3: PlugSPL Feature Model Edi
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contributions from the several acto
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o Guideline 6.4: Softgoal dependenc
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descriptions must also b e configur
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It means that throughout the develo
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B. Instrumentation The experiment w
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• External Validity: W e have con
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II. THE PROPOSED TAXONOMY The taxon
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sub-dimension is categorized as, co
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A Proposal of Reference Architectur
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Figure. 1. Reference Architecture M
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A Variability Management Method for
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C. Correctness of configuration fil
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means patterns which are shown in s
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Tool Support for Anomaly Detection
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Figure 1. System overview of the SD
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Once the datasets were evaluated us
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Reconfiguration of Robot Applicatio
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data dependencies required for keep
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Spacemaker: Practical Formal Synthe
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Fig. 1. The ecommerce object model.
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Fig. 3. Mapping diagram for Figure
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A formal support for incremental be
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machine may be empty which should l
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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
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
Page 769 and 770: as an add-in to popular UML m odeli
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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
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Page 789 and 790: With the GDUC approach, we can mode
Page 791 and 792: Figure 6. Three proposals containin
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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: Checking Contracts for AOP using XP
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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