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VI. CONCLUSIONS AND FUTURE WORK One-use components and their equivalent reuse components were analyzed for their sizes and the number of parameters. Both of these were found to be significantly higher for the reusable components. Programmer effort in terms of hours was also analyzed and observed to be signif icantly higher for reusable components. The productivity in SLOC/hour for the SLOC difference of reusable components was significantly lower than those for the one-use components. The higher SLOC for the reusable components is due to the additi onal functionality of the reusable components. The productivity was also affected by the additional functionalities introduced in the reusable components as the productivity was lower for the reusable components. This may be because the subjects might have spent more time in designing for the reusable components and modifying the existing one-use components as they are following the reengineering process for reuse design and not the build from scratch method. The effort required for reusable components was also sig nificantly higher than those for the one-use components. The number of parameters was significantly higher for the reusable components than those for the one-use components. This may be because of more code within the com ponent to realize the additional functionalities for reusability. Nineteen reuse design principles were taught to the subjects. The most frequently used based on pareto ranking of the principles were presented. The six most frequently used reuse design principles were – well-defined interface, link to documentation, clarity and understandability, generality, separate concepts from contents and commonality and variability. The reuse design principles of isolation of context and policy from functionality, abstraction and self-documenting code were least used by the subjects. This may be because the components developed were relatively simple. Future work may include an empirical study using a more complex algorithm. Future work may include components built in various other languages as well. Design with reuse may also be studied in future by having subjects use the reusable components developed in this study. The study will focus on identifying whether a correlation exists between the reu se design principles used and the ease of reusability. [1] W. B. Frakes and D. Lea, "Design for Reuse and Object Oriented reuse Methods," presented at the Sixth Annual Workshop on Institutionalizing Software Reuse (WISR '93), Owego, NY, 1993. [2] J. Sametinger, Software engineering with reusable components. Berlin Heidelberg, Germany: Springer Verlag, 1997. [3] B. Weide, et al., "Reusable software components," Advances in computers, vol. 33, pp. 1-65, 1991. [4] M. Ezran, et al., Practical software reuse: the essential guide. London: Springer Verlag, 2002. [5] W. B. Frakes and K. C. Kang, "Software reuse research: status and future," IEEE Transactions on Software Engineering, vol. 31, pp. 529- 536, 2005. [6] W. Frakes and C. Terry, "Software reuse: metrics and models," ACM Comput. Surv., vol. 28, pp. 415-435, 1996. [7] R. van Ommering, "Software reuse in product populations ," IEEE Transactions on Software Engineering, vol. 31, pp. 537-550, 2005. [8] P. Mohagheghi and R. Conradi, "Quality, productivity and economic benefits of software reuse: a review of industrial studies," Empirical Software Engineering, vol. 12, pp. 471-516, 2007. [9] P. Mohagheghi and R. Conradi, "An empirical investigation of software reuse benefits in a large telecom product," ACM Transactions on Software Engineering Methodology, vol. 17, pp. 1-31, 2008. [10] W. B. Frakes and G. Succi, "An industrial study of reuse, quality, and productivity," Journal of Systems and Software, vol. 57, pp. 99-106, 2001. [11] M. Morisio, et al., "Success and Failure Factors in Software Reuse," IEEE Transactions on Software Engineering, vol. 28, pp. 340-357, 2002. [12] W. C. Lim, "Effects of Reuse on Quality, Productivity, and Economics," IEEE Softw., vol. 11, pp. 23-30, 1994. [13] M. D. McIlroy, et al., "Mass produced software components," Software Engineering Concepts and Techniques, pp. 88–98, 1969. [14] R. Seepold and A. Kunzmann, Reuse techniques for VLSI design. Netherlands: Springer 1999. [15] L. Latour, et al., "Descriptive and predictive aspects of the 3Cs model: SETA1 working group summary," 1991, pp. 9-17. [16] W. B. Frakes and R. Baeza-Yates, Information retrieval: data structures and algorithms, 2nd ed. vol. 77. Englewood Cliffs, NJ: Prentice-Hall. , 1998. [17] B. Boehm, et al., "Software development cost estimation approaches — A survey," Annals of Software Engineering, vol. 10, pp. 177-205, 2000. [18] B. W. Boehm, Software engineering economics. Upper Saddle River, NJ: Prentice-Hall, 1981. [19] B. Boehm, et al., "Cost estimation with COCOMO II," ed: Upper Saddle River, NJ: Prentice-Hall, 2000. [20] L. H. Putnam and W. Myers, Measures for excellence: Yourdon Press, 1992. [21] R. Jensen, "An improved macrolevel software development resource estimation model," in 5th ISPA Conference, 1983, pp. 88–92. [22] R. W. Selby, "Enabling reuse-based software development of large-scale systems," IEEE Transactions on Software Engineering, vol. 31, pp. 495- 510, 2005. [23] T. Tan, et al., "Productivity trends in incremental and iterative software development," in ESEM '09 Proceedings of the 2009 3rd International Symposium on Empirical Software Engineering and Measurement Lake Buena Vista, Florida, USA, 2009, pp. 1-10. [24] A. Gupta, "The profile of software changes in reused vs. non-reused industrial software systems," Doctoral Thesis, NTNU, Singapore, 2009. [25] N. E. Fenton and M. Neil, "Software metrics: roadmap," presented at the Proceedings of the Conference on The Future of Softw are Engineering, Limerick, Ireland, 2000. [26] M. Dinsoreanu and I. Ignat, "A Pragmatic Analysis Model for Software Reuse," in Software Engineering Research, Management and Applications 2009. vol. 253, R. Lee and N. Ishii, Eds., ed: Springer Berlin / Heidelberg, 2009, pp. 217-227. [27] I. Herraiz and A. E. Hassan, "Beyond Lines of Code: Do We Need More Complexity Metrics?," in Making Software: What Really Works, and Why We Believe It, A. Oram and G. Wilson, Eds., 1st ed Sebastapol, CA: O' Reilly Media, Inc. , 2010, pp. 125-141. [28] T. J. McCabe, "A Complexity Measure," IEEE Transactions on Software Engineering, vol. SE-2, pp. 308-320, 1976. [29] S. H. Kan, Metrics and models in software quality engineering. India: Pearson Education India, 2003. [30] J. Graylin, et al., "Cyclomatic Complexity and Lines of Code: Empirical Evidence of a Stable Linear Relationship," Journal of Software Engineering and Applications, p. 137, 2009. [31] R. P. L. Buse and W. R. Weimer, "Learning a Metric for Code Readability," IEEE Transactions on Software Engineering, vol. 36, pp. 546-558, 2010. [32] J. E. Gaffney, "Estimating the Number of Faults in Code," IEEE Transactions on Software Engineering, vol. SE-10, pp. 459-464, 1984. [33] M. S. Krishnan and M. I. Kellner, "Measuring process consistency: implications for reduci ng software defects," IEEE Transactions on Software Engineering, vol. 25, pp. 800-815, 1999. [34] W. B. Frakes and T. P. Pole , "An empirical study of representation methods for reusable software components," IEEE Transactions on Software Engineering, vol. 20, pp. 617-630, 1994. [35] J. Cohen, Statistical power analysis for the behavioral sciences, 2nd ed. Hillsdale, NJ: Lawrence Erlbaum Associates, Inc., 1988. 199
Choosing licenses in free open source software Ioannis E. Foukarakis Department of Telecommunications Science and Technology University of Peloponnese Tripolis, Greece Georgia M. Kapitsaki Department of Computer Science University of Cyprus Nicosia, Cyprus Nikolaos D. Tselikas Department of Telecommunications Science and Technology University of Peloponnese Tripolis, Greece Abstract—Free/Libre/Open Source Software (FLOSS) has been lately on the focus of the software community in the attempt to provide wide access to software resources and promote their distribution. Open software resources may be linked directly or indirectly with more than one open source licenses. Due to license heterogeneity, incompatibilities may arise in such dependencies. For this reason, adequate support for decision making in license use is an important issue. Such systems can assist in determining the right licenses and avoid potential violations. We tackle the above aspects by giving an overview of the areas of license extraction from software resources, license information storing and license-related reasoning activities. We discuss about several attempts in these areas and present an initial prototype assistance system employing such techniques. Keywords- data dependencies; licensing; open source software I. INTRODUCTION Free/Libre/Open Source Software (FLOSS) [1] has influenced modern software engineering processes, allowing individuals to use software for free and software engineers to incorporate third party software libraries in their software products and implementations. The terms, under which the software has become available and is provided for use to the community, are captured in the licenses that accompany open source software. Licenses correspond to the characteristics of the intellectual property and usually express the rights and the obligations of the copyright owner and the one using the license (referred to as the licensee). Many different licenses have appeared: GNU General Public License (GPL), Apache License, BSD License, MIT License to name a few. Licenses fall into three main categories: permissive, weak copyleft or strong copyleft, showing different degrees of freedom in FLOSS use. The first category of permissive licenses states that licensed software can be adopted even in commercial software that is not distributed as open so urce. On the contrary, weak copyleft licenses demand that any changes in the licenses source code should be made available as open source software itself, whereas strong copyleft licenses mandate that any kind of further distribution be under open source principles. Each license may have multiple versions making it difficult for developers to cope with incompatibilities that might exist due to the use of software libraries based on different licenses. Especially during the development phase, it is often the case that engineers include additional, and often redundant, dependencies in their code light-hearted, without examining possible licensing incompatibilities. As the number of components increases, so does the complexity of deciding which licenses can be applied to the final system, or of checking if violations exist among the terms defined in the licenses, leading to license incompatibility. Lindberg [2] describes license compatibility as a sim ilar approach with blood type compatibility: “two blood types are compatible if donations from two different people can be used together.” In the same way, licenses are compatible if software distributed under two different licenses can be used together in the same software product. Although no mature software for supporting decisions in license issues is available, many research activities and commercial efforts have recently appeared. The current paper provides an overview of the aspects of license decisions by presenting the process of license compatibility detection that can be divided into the areas of license extraction information from software resources, license information storing, as well as modeling and reasoning actions. Some techniques are applied on the implementation of a prototype system extending a popular build tool. The rest of the paper is structured as follows. Section II briefly presents the steps of the license compatibilities process. Section III discusses works on extracting license information from existing components, whereas section IV is dedicated to the presentation of repository-based approaches. In section V ways for discovering licenses are presented. An initial prototype implementation of the compatibility detection is demonstrated in section VI. Finally, section VII concludes the paper. II. THE LICENSE COMPATIBILITY PROCESS Each software system is comprised of individual artifacts that may include source code, compiled programs, software libraries or other file types (i.e., images, multimedia files and data in general). An abstract model of the software includes the distinct artifacts and the way these are connected through the development process. For instance, a source code file may hold references to other source code files or libraries. In turn, the compiled version of the source code may have static or dynamic references to software libraries and so on. The generic approach that can be followed for detecting license incompatibilities is depicted in Fig. 1. The first step towards making a decision for possible open source licenses is to identify the individual components of the software system. Afterwards, different license extraction techniques may be applied to detect the license of each artifact, varying from text matching to querying license repositories. In fact, the same 200
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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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A never-ending language learner cal
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4. i 2 Learning(DEC, mex) via Bias
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4.4. An Illustrative Example Now le
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Evolutionary Learning and Fuzzy Log
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In addition to the rules, the knowl
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As described in the previous sectio
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the ontology hierarchy; learning a
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three different learning techniques
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TABLE IX. USING SVM FOR LEARNING tr
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Siemens set contains seven C progra
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The program tcas is not included be
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esults. To be more specific, they w
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According to IEEE [10], regression
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(a) Volatility (b) Complexity (c) R
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It mimics the test engineer knowled
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integrate the test results. • Aut
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No tify the cloud controller to get
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of execution-data classification. T
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Figure 1. Boxplots of average AUC r
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anches is more applicable than the
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To validate the proposed approach,
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contains broadcast of Subject to Ob
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S 1a=1; S 2b=5; cobegin { S 3read
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.
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298
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A functionality is the ability of a
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level); in UML/J2EE technology [11]
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• to provide a unified catalogue
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flexibility they provide to model s
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complete in zero-time, and thus hav
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figure assumes the experiment with
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could be a practical way of dealing
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and (iv) the reduced amount of line
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III.BUSINESS RULES FRAMEWORK FOR KN
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explanation for why the program wan
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A model introducing SOAs quality at
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there exist a hierarchical ranking
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Software as a Service: Undo Hernán
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operations consist in reinjection i
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stored. If event failure, external
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ensure users the trustworthiness of
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Patient Doctor Doctor Patient Direc
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going down at a time. This is a rea
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Decidability of Minimal Supports of
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A S 1 1 1 0 0 0 1 1 0 0 0 1 1
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satisfies R( D) R( C) S 1. Compa
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Automated Generation of Concurrent
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target of testing. A blackbox test
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sequence. Each t i θ i in the firi
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SAMAT -AToolforSoftware Architectur
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Figure 4. The SAM Hierarchical Mode
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High-level Petri net Place Place Ty
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verification and thus is often limi
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(r 1 r 2 ...r k ) 1 denotes Path [r
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Fig. 3. Example a 3-compound weakly
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necessary before verification. In t
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CTL formula as: AG (( jp EX( jp ad
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equirements level, like EA-Miner [1
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II. RESOURCE-ORIENTED WORKFLOW MODE
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eventually triggers a co mmon task.
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Input: A resource oriented workflow
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access control exists, the action i
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(ACCDA), is customized to address a
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ICrudSchema and shown in Figure 3.
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Connectors for Secure Software Arch
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Non-repudiation security service pr
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anAsynchronousCustomer InterfaceCon
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How Social Network APIs Have Ended
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OSN Social Feed Unique Features Con
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users can control the reach of thei
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Computer Forensics: Toward the Cons
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In 2009, Cohen et al. in [4] have o
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dcterms:contributor, dcterms:creato
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A Holistic Approach to Software Tra
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Figure 1. Figure 1 shows an overvie
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B. Case study in a proprietary proj
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Pointcut Design with AODL Saqib iqb
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system attributes, methods and exec
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Feature modeling and Verification b
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hasOptionalPart hasPart hasMandato
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A Context Ontology Model for Pervas
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Figure 1. UML view of the model. Th
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Figure 3. Architecture for deliveri
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Ontology-based Representation of Si
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Figure 2. Upper Ontology fragment f
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I2Sim simulation model was transfor
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An ontology-based approach for stor
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fying the equivalences between conc
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• Homonymy conflicts: occur when
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Automatic Generation of Architectur
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we dealt w ith the verticals rules.
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Towards Architectural Evolution thr
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different from replacing it, we dec
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Using FCA-based Change Impact Analy
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Table 2. Impact set of C1,C2,C5 cha
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Figure 3. The PF and PTS results fo
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Forecasting Fault Events in Power D
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which the airport data did not cont
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classified. Recall is the probabili
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Testing Interoperability Security P
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Figure 2. Overview of the Test Gene
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Objective Figure 4. Testing
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A New Approach to Evaluate Path Fea
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k v ( df i ) 0 , it means that the
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Model & Method Feasibility Evaluati
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Structural Testing for Multithreade
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adequate to another criterion. This
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480
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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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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
Page 809 and 810:
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
Page 815 and 816:
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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