SEKE 2012 Proceedings - Knowledge Systems Institute

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Samedi Heng, 299 E. Hernandez-Garcia, 763 Estevão R. Hess, 551 Flávio E. A. Horita, 225 Alejandro Hossian, 25 Wenhui Hu, 541 Yen-Chieh Huang, 759 Zhiqiu Huang, 422 Marianne Huchard, 118 Chengfeng Hui, 139 Reisha Humaira, 480 Mamoona Humayun, 131 I Saqib Iqbal, 418 J Kushal Jangid, 13 Shunhui Ji, 231, 235, 239 Nahla JLAIEL, 505 Seungwook Jung, 684 K Taeghyun Kang, 394, 684 Georgia M. Kapitsaki, 200 Takuya Katayama, 672 Taghi M. Khoshgoftaar, 74, 94, xxvii Sunghoon Kim, 684 Manuel Kolp, 299 Andreas Krall, 19 Josiane Kroll, 551 Uirá Kulesza, 557 L Thomas Lambolais, 694 Olivier Le Goaer, 517 Sarah B. Lee, 700 Yu Lei, 486 Thiago Leite, 574 Bixin Li, 7, 231, 235, 239, 452 Demin Li, 381 Jiakai Li, 231, 235, 239 Kuwen Li, 106 Sihan Li, 267 Xuandong Li, 369 Feng Li, 279 Yao-Nan Lien, 359 Ricardo Lima, 148 Yachai Limpiyakorn, 724 Jugurta Lisboa-Filho, 511 Alan Liu, 759 Gaiyun Liu, 359 Hui Liu, 55 Jia Liu, 139 Jing Liu, 61 Shih-Hsi Liu, 537 Su Liu, 352 Xi Liu, 369 Yi Liu, 55 Wei Liu, 279 Xiaoqiang Liu, 279 Zhenyu Liu, 279 Luis Llana, 747 Luanna Lopes Lobato, 641 Hakim Lounis, 406 S. Lozano-Fuentes, 763 Faming Lu, 340 Wo-Shun Luk, 706 Hong-Viet Luong, 694 M Marianella Aveledo M., 599 Jiaying Ma, 135 Weiyun Ma, 139 Wentao Ma, 289 Zhiyi Ma, 55 Mazen EL Maarabani, 464 Ivan do Carmo Machado, 641 Marco Antonio Machado, 491 Marcelo de Almeida Maia, 174 Dwijesh Dutta Majumder, 315 José Carlos Maldonado, 657 José C. Maldonado, 622, 647 Bhavya Malhotra, 394 Narendar Mandala, 45 Guilherme A. Marchetti, 563 José R. Marti, 432 Rivalino Matias Jr., 174 Robert McCartney, 162 Danilo Medeiros, 545 Rafael Pinto Medeiros, 635 Silvana M. Melo, 476 Hernán Merlino, 328 Marjan Mernik, 537 Yi Miao, 267 Jing-Yeu Miaw, 100 A-8
Seyedehmehrnaz Mireslami, 70 Takao MIURA, 741 Peter Molin, 205 Jefferson Seide Molléri, 143 Thomas Moser, 19, 730 Mohammad Moshirpour, 70 Alexandre Mota, 148 Bruno de Azevedo Muniz, 80 Patrícia Fontinele Muniz, 311 Leonardo Gresta Paulino Murta, 635 N Elisa Yumi Nakagawa, 158 Amri Napolitano, 74, 94 Satoshi NARATA, 741 Charoensak Narkngam, 724 Leandro Marques Nascimento, 311 Crescencio Rodrigues Lima Neto, 628 Júlio Cesar Campos Neto, 80 Paulo Anselmo Mota Silveira Neto, 628, 641 Pedro Santos Neto, 273, 545 Mahdi Noorian, 663 Daltro J Nunes, 570 Amjad Nusayr, 154 O Omar Ochoa, 39 Akira Ohashi, 480 Alcione de Paiva Oliveira, 511 César Oliveira, 148 Karolyne Oliveira, 531 Edson A. Oliveira Junior, 622, 647 Lenin Ernesto Abadie Otero, 311 Mourad Oussalah, 324 Keizo Oyama, 124 P Rebecca J. Passonneau, 100 Erick Passos, 545, 712 Oscar Pastor, 531 Pratik Paul, 13 Witold Pedrycz, 106 Cecília Sosa Arias Peixoto, 594 Johannes Pelto-Piri, 205 Óscar Mortágua Pereira, 387 Angelo Perkusich, 426 Patricia Pesado, 328 Thanh-Liem Phan, 694 João Pimentel, 444, 448 Vládia Pinheiro, 574 Fábio Pittoli, 570 Eduardo Kessler Piveta, 438 D. R. Plante, 499 Rafael Prikladnicki, 551 Fábio Protti, 635 Q Xiaofang Qi, 289, 293 Ju Qian, 289 YongJun Qie, 180 R Ricardo Rabelo, 273 Axinia Radeva, 100 Filip Radulovic, 611 M. Rahmani, 305 Deepa Raka, 537 Lakshmi Ramachandran, 458 Sébastien Ramon, 243 Henrique Rebêlo, 148 Marek Z. Reformat, 106, xxviii Márcio Ribeiro, 148 Luis Rivero, 582 Ana Regina Cavalcanti Rocha, 753 Elder M. Rodrigues, 112, 647 Evandro Luis Linhari Rodrigues, 668 Myungchan Roh, 684 Cynthia Rudin, 100 S Ana M. Moreno S., 599 Deise de Brum Saccol, 438 D. Sadhu, 763 S. Masoud Sadjadi, 88, xxvi Kazunori Sakamoto, 480 Salamah Salamah, 39 Hamza Onoruoiza Salami, 737 Afonso Sales, 551 Alan R. Santos, 551 Emanuel Santos, 448 Maribel Yasmina Santos, 387 Pedro Santos Neto, 545 Eber Assis Schmitz, 319 Manan R. Shah, 578 Lingshuang Shao, 211 Weizhong Shao, 55 A-9
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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
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ecordProblem → A → reproducePro
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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
Page 785 and 786: 756
Page 787 and 788: 758
Page 789 and 790: With the GDUC approach, we can mode
Page 791 and 792: Figure 6. Three proposals containin
Page 793 and 794: 764
Page 795 and 796: 766
Page 797 and 798: Proactive Two Way Mobile Advertisem
Page 799 and 800: information. If more than one adver
Page 801 and 802: Users can al so publish adv ertisem
Page 803 and 804: The COIN Platform: Supporting the M
Page 805 and 806: A-3
Page 807 and 808: Checking Contracts for AOP using XP
Page 809: Maicon B. da Silveira, 112 Aldo Dag
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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