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4.3.2 Impact on the Effectiveness of Fault Localization We use box plots to depict the overall experimental results. It takes p (the ratio of the number of clusters) as a parameter, and p = 6%. Each box plot represents the statistics of the T- score reduction for each subject program. The bottom and top of the box are 25th and 75th percentile, respectively. The line within the box denotes the median of the values in the box and the point denotes the mean. The ends of the whiskers represent the minimum and maximum of all the data. Figure 2 and 3 illustrates the impact of our approach on the effectiveness of CBFL. Figure 2 depicts the results applying the cleaning strategy, and Figure 3 depicts the results applying the relabeling strategy. The ideal situation, where all the coincidentally correct test cases are picked out, with 0% false negatives and 0% false positives, is also shown on the figures as a comparison. The bold dot presents the average T-score reduction for each program under the ideal situation. 19.57% versions have been improved by the cleaning strategy, and the T-score reduction can reach up to 8.55%. 33.33% versions have been improved by the relabeling strategy, and the T-score reduction can reach up to 22.0%. In summary, as shown in Figure 2 and 3, the cleaning strategy is relatively a safe method, because if p is set to a reasonable value, 6% in our case, more than 87% versions will be improved or stay the same, with an increase rate of 0.67%~8.55%, while the rest 13% may be deteriorated, with the decrease rate of -2.0% ~ -0.41%. The effectiveness of fault localization of some versions remains the same. We observe that the faulty statements in most of the versions have already been ranked at the very front position, such that dealing with the coincidental correctness will have little effect on the improvement. Moreover, as presented in W. Masri et al., [5], although cleaning coincidental correctness will lead to an increment of the suspiciousness metric of the faulty statement, the rank of the statement will not necessarily increase correspondingly. Therefore, the effectiveness of CBFL after applying our methodology remains the same or even gets worse for some versions. The key factors that influence the improvement of CBFL are the rate of false negatives and false positives, which are heavily depended on the clustering results. Furthermore, we conducted a paired t-test on the differences between the T-scores before and after applying our strategy. The paired t-test is a statistical technique that is used to compare two population means in the case of two samples that are correlated, especially in a “before- after” study. Suppose the T-score before using our strategy is A, and the T- score after using our strategy is B. H0: A B. Note that, during this test, only T-scores less than 20% are taken into account. The reason is that it is not common to ask programmers to examine more than 20% of the code in practice [11]. It can observed that, by using cleaning strategy and relabeling strategy, the p-value is 0.01 and 0.02, respectively, both of which implies that the improvement is significant at the 0.05 level. T-score reduction% 40 35 30 25 20 15 10 5 0 -5 Ideal Cleaning Strategy -10 printtokens2 printtokens replace schedule2 schedule totinfo Figure 2. Impact of cleaning strategy on the effectiveness of CBFL T-score reduction% 50 40 30 20 10 0 Ideal Relabeling Strategy -10 printtokens2 printtokens replace schedule2 schedule totinfo Figure 3. Impact of relabeling strategy on the effectiveness of CBFL Statistical test is also conducted on the results of the ideal situation. Under the ideal situation, using cleaning and relabeling strategy, the p-value is 3.43*10 -4 and 9.71*10 -17 respectively, and both of them indicate a very significant improvement. The above result shows that it is promising to improve the effectiveness of CBFL by dealing with coincidentally correct test cases. And using our approach, the preliminary experimental result is encouraging and convincing. 4.4 Threads to Validity The threats to external validity include the use of Siemens set as our subject programs. As a matter of fact, these programs are all small C programs and the faults are manually injected. To reduce this threat, we plan to apply our technique to larger programs in the future. The threats to internal validity include the tools we use to generate execution profiles and conduct the cluster analysis. In our context, we use gcov to record coverage information and rely on the data mining tool Weka for cluster analysis. Both of them are mature and widely used. Another issue related to internal validity is the clustering algorithm we choose. As in our study, we use simple K-means because it is simple and effective. However, having the failed test cases clustered either too centralized or too scattered will have adverse effect on the 271
esults. To be more specific, they will lead to high false negative and false positive rates, respectively. 5 RELATED WORK Voas [2] introduced the PIE model, which emphasizes that for a failure to be observed, the following three conditions must be satisfied: “Execution”, “Infection”, and “Propagation”. W. Masri et al. [3] have proved that coincidental correctness is responsible for reducing the safety of CBFL. As shown in the previous studies [4, 5], the efficiency and accuracy of CBFL can be improved by cleaning the coincidentally correct test cases. However, it is challenging to identify coincidental correctness because we do not know the location of fault beforehand. X. Wang et al. [4] have proposed the concept of context pattern to help coverage refinement so that the correlation between program failures and the coverage of faulty statements can be strengthened. W. Masri et al. [5] have presented variations of a technique that identify the subset of passed test cases that are likely to be coincidentally correct. One of these techniques first identifies program elements (cc e ) that are likely to be correlated with coincidentally correct test cases. Then it categorizes test cases that induce some cc e s as coincidental correctness. The set of coincidentally correct test cases would be partitioned into two clusters further. A more suspicious subset will be cleaned to improve the effectiveness of fault localization. The experimental result is promising, however, although it used the same subject program (the Siemens test suite) as ours in their experiment, it is applicable to only 18 versions of the 132 versions, which has a smaller application scope than our approach (applicable to 66 out of 132 versions). Previous empirical observations have shown that, by cluster analysis, test cases with similar behaviors could be grouped into the same clusters. Therefore, cluster analysis has been introduced for test case selection. Vangala et al. [12] used program profiles and static execution to compare test cases and applied cluster analysis on them, identifying redundant test cases with high accuracy. Dickinson et al. introduced cluster filtering technique [7, 13]. It groups similar execution profiles into the same clusters and then selects a subset of test cases from each cluster based on a certain sampling strategy. Since test cases in the same cluster have similar behaviors, the subsets are representative for the test suite so that it is able to find most faults by using the selected subsets instead of the whole test suite. 6 CONCLUSIONS AND FUTURE WORK In this paper, we proposed a clustering-based strategy to identify coincidental correctness from the set of passed test cases. To alleviate the adverse effect of coincidental correctness on the effectiveness of CBFL, two strategies, either removing or relabeling, were introduced to deal with the identified coincidentally correct test cases. We conducted an experiment to evaluate the proposed approach. The experimental results suggested that it achieved approximate results as the ideal situation did. We intend to conduct more comprehensive empirical studies and explore the following issues in our future work: 1) Search for better clustering algorithms to fit in with this scenario. As denoted in section 4.4, the failed test cases clustered either too centralized or too scattered would lead to poor results. We use K-means in our experiment for its simplicity, and there are many other clustering algorithms need to be explored. 2) Conduct empirical studies on how multiple-faults affect the result of our approach and explore how to deal with this situation to minimize the adverse effects. REFERENCES [1] J.A. Jones and M. J. Harrold, “Empirical evaluation of the tarantula automatic fault-localization technique”, ASE, 2005, pp.273-282. [2] J.M. Voas, “PIE: A dynamic failure-based technique”, IEEE Trans. Softw. Eng., 1992, pp.717-727. [3] W. Masri, R. Abou-Assi, M. El-Ghali and N. Fatairi. Nour, “An empirical study of the factors that reduce the effectiveness of coveragebased fault localization”, ISSTA, 2009, pp.1-5. [4] X. Wang, S. Cheung, W. Chan and Z. Zhang, “Taming coincidental correctness: Coverage refinement with context patterns to improve fault localization”, ICSE, 2009, pp.45-55. [5] W. Masri and R. Abou-Assi, “Cleansing test suites from coincidental correctness to enhance fault-localization”, ICST, 2010, pp.165-174. [6] S. Yan, Z. Chen, Z. Zhao, C. Zhang and Y. Zhou, “A dynamic test cluster sampling strategy by leveraging execution spectra information”, ICST, 2010, pp.147-154. [7] W. Dickinson, D. Leon and A. Podgurski, “Finding failures by cluster analysis of execution profiles”, ICSE, 2001, pp.339-348. [8] C. Zhang, Z. Chen, Z. Zhao, S. Yan, J. Zhang and B. Xu, “An improved regression test selection technique by clustering execution profiles”, QSIC, 2010, pp.171-179. [9] R. Abreu, P. Zoeteweij, R. Golsteijn, A. J.C. van Gemund, “A practical evaluation of spectrum-based fault localization”, Journal of <strong>Systems</strong> and Software, Volume 82, Issue 11, 2009, pp. 1780-1792. [10] B. Liblit, M. Naik, A. Zheng, A. Aiken and M. Jordan, “Scalable statistical bug isolation”, PLDI, 2005, pp.15-26. [11] C. Liu, X. Yan, L. Fei, J. Han and S. Midkiff, “SOBER: statistical model-based bug localization”, ESEC/FSE, 2005, pp.286-295. [12] V. Vangala, J. Czerwonka and P. Talluri, “Test case comparison and clustering using program profiles and static execution”, ESEC/FSE, 2009, pp. 293-294. [13] W. Dickinson, D. Leon and A. Podgurski, “Pursuing failure: the distribution of program failures in a profile space”, ESEC/FSE, 2001, pp. 246-255. [14] V. Debroy, W. Eric Wong, X. Xu, B. Choi, “A grouping-based strategy to improve the effectiveness of fault localization techniques”, QSIC,2010, pp.13-22. [15] T. Denmat, M. Ducassé and O. Ridoux, “Data mining and crosschecking of execution traces: a re-interpretation of Jones, Harrold and Stasko test information”, ASE, 2005, pp. 396-399. [16] L. Naish, H. J. Lee, and K. Ramamohanarao, A model for spectra-based software diagnosis, TOSEM, in press. [17] Software-artifact Infrastructure Repository. http://sir.unl.edu/, University of Nebraska. [18] http://gcc.gnu.org/onlinedocs/gcc/Gcov.html [19] http://en.wikipedia.org/wiki/Euclidean_distance 272
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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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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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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
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Maicon B. da Silveira, 112 Aldo Dag
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Seyedehmehrnaz Mireslami, 70 Takao
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Wei Zhang, 422 Wenbo Zhang, 188 Zhi
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Desmond Greer Eric Gregoire Christi
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Poster/Demo Presenter’s Index A A
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SEKE 2012 Proceedings - Knowledge Systems Institute

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