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However, when we observed the total points of correct elements for each map, prescriptive teams have more accuracy. We also computed the quality metric, associated to RQ3: Adaptive: 4.17/6 = 70% Prescriptive: 3.38/4 = 85% Our findings indicate that teams using adaptive approach had 15% less quality; in contrast, these teams had 16% more speed. This means that there is a trend showing that teams using adaptive approaches might have less quality but more speed. However, we did not have enough data points to support statistical analysis. V. DISCUSSION FTS is a r esearch area with many important aspects to investigate [3] [4] [13]. In theory, the use of FTS can significantly reduce the duration of the software development lifecycle [14]. In this study, we investigated the difference and benefits of u sing prescriptive and adaptive approaches in the context of FTS. The speed obtained by teams using an adaptive approach is higher than the speed obtained by teams using a p rescriptive approach. The percentage obtained by adaptive teams was 30% higher than the speed obtained by prescriptive teams. Phalnikar, Deshpande and Joshi [15] argue that agile teams are able to produce software faster, and Smite, Moe and Agerfalk [16] state that adaptive approaches such as agile methodologies aim to increase productivity of software teams [16]. Thus, adaptive approaches could benefit FTS by reducing the project cycle time. Moreover, FTS aims to reduce time-to-market [4], which it seems that can be obtained using adaptive approaches. The accuracy obtained by teams in the delivered tasks was better in two tasks performed by prescriptive teams when we compare the first three tasks. On the other hand, when comparing the average accuracy of delivered tasks, adaptive teams are better than prescriptive teams. Teams using adaptive approaches are able to implement more requirements than teams using prescriptive approaches. This result shows that adaptive approaches may accelerate communication between teams and thus increasing the productivity, but we observed a lack of formal protocols to check the accuracy of the work. The number of sites interacting in a development cycle may cause a small impact on the average accuracy [8]. We observed that the perception in relation to a task delivered is also a factor that affects the accuracy of the work. Our findings showed that students had less perception on task accuracy using adaptive approaches. Quality is also an important factor for project success [5]. We observed this factor verifying the total accuracy points and the total number of maps delivered by each team. Results obtained showed that adaptive teams had 15% less quality than prescriptive teams. Carmel, Espinosa and Dubinsky [4] obtained similar result in their quasi-experiment. The decreasing of quality in maps delivered by teams using adaptive approaches could be caused by the lack of participant’s perception in relation to the quality criteria required for each task. Based on the results found, our experiment suggests that adaptive approaches could perform better than prescriptive approaches in the context of FTS. This is also claimed by Carmel, Espinosa, and Dubinsky [4] and Gupta [17], and should be deeply investigated in the future, with experiments also executed in real software development settings. VI. THREATS TO VALIDITY One of the key issues in experimentation is evaluating the validity of the results [11]. In this section we discuss the potential threats that are relevant for our study and how they are addressed. A. Construct Validity Construct validity concerns the degree of accuracy to which the variables defined in the study measure the constructs of interests. In this study, we have followed the constructs defined in the two original FTS experiments [8] [12]. B. Internal Validity Threats to internal validity influence the conclusions about a possible causal relationship between the treatment and the outcome of a study. We identified a couple of s uch kind of threats. Skill to perform the task: selected students had different skills and could potentially influence tasks performance. We randomly select students for each approach to minimize this threat. Experiment unit: we used an experimental condition different from a real scenario of s oftware development. Students’ iteration included additional tasks, which are not usually done in a typical “real” iteration. Measures: our measures can be imperfect, since we simulate a full day work with only few minutes. We acknowledge that, but observe that any experiment would need to drastically reduce effort to represent a full day in an experimental setting. The type of task: participants manipulate maps and do not carry out real programming tasks. In this case, we refer to the original experiments where the authors indicate that they “decided in favor of a fi tional map task instead to eliminate possible confounds due to differences in the software programming abilities of the participants” [8] [12]. C. External Validity External validity describes the study representativeness and the ability to generalize the results outside the scope of the study. For any academic laboratory experiment the ability to generalize results to industry practice is restricted by the usage of students as study participants. Although the students may not be representative of the entire population of s oftware professionals, it has been shown that the differences between students and real developers may not be as large as assumed by previous research [19]. 555
D. Conclusion Validity Conclusion validity is concerned with the relationship between the treatment and the outcome. We acknowledge that the small number of data points is not ideal from the statistical point of vi ew. Small sample sizes, especially when the key experimental unit is at th e team level, are a k nown problem difficult to overcome. VII. CONCLUSION AND FUTURE WORK In this paper, we executed an experiment in order to investigate both the adaptive and prescriptive approaches in the context of FTS software development. We found that the usage of adaptive approaches increases the speed, but they do not always enhance accuracy and quality of the work done by distributed sites. We believe that this experiment has important findings that contribute to the literature on global software engineering and follow the sun software development. For future work, we suggest new studies in order to replicate this experiment with more participants and different shifts scenarios (e.g. some time-zone overlap). We also suggest the replication of this experiment with industry participants. ACKNOWLEDGMENT The authors are funded by the PDTI program, funded by Dell Computers of Brazil Ltd. (Law 8.248/91). The third author is also funded by Ci&T and CNPq (projects 483125/2010-5 and 550130/2011-0. The authors thank all the students who participated in the experiment and Alberto Espinosa, Ning Nan and Erran Carmel for providing us the material used in their experiment. REFERENCES [1] P. Sooraj, P. K. J. Mohapatra, "Modeling the 24-h software development process". Strategic Outsourcing: An International Journal, 122-141, 2008. [2] E. Carmel, J. Espinosa, Y. Dubinsky, "Follow the Sun Workflow in Global Software Development,” Journal of Management Information Systems Vol. 27 No. 1, 2010, 17 – 38. [3] J. Kroll, E. Hess, J. L. N. Audy, and R. Prikladnicki, “Researching into Follow-the-Sun Software Development: Challenges and Opportunities,” In: 6th International conference on Global Software Engineering (ICGSE), 2011, Helsinki, Finland. [4] E. Carmel, A. Espinosa, Y. Dubinsky, “Follow The Sun Software Development: New Perspectives, Conceptual Foundation, and Exploratory Field Study,” 42nd Hawaii International Conference on System Sciences, Proceedings, 2009. [5] R. Jabangwe, I. Nurdiani, “Global Software Development Challenges and Mitigation Strategies: A Systematic Review and Survey Results”. Master´s program in Software Engineering, Blekinge Institute of Technology, OM/School of Computing, 2010. [6] H. Holmstrom, E. O. Conchuir, P. J. Agerfalk, B. Fitzgerald, “Global Software Development Challenges: A Case Study on Temporal, Geographical and Socio- Cultural Distance”. Proceedings of the IEEE international conference on Global Software Engineering (ICGSE '06). IEEE Computer Society, Washington, DC, USA, 2006, 3-11. [7] M. Yap, "Follow the sun: distributed extreme programming development," Agile Conference Proceedings, 2005, 218- 224. [8] V. R. Solingen, M. Valkema, “The Impact of Number of Sites in a Follow the Sun Setting on the Actual and Perceived Working Speed and Accuracy: A Controlled Experiment”. Global Software Engineering (ICGSE), 5th IEEE International Conference,165- 174, 2010. [9] C. Visser, R. V. Solingen, “Selecting Locations for Follow-the- Sun Software Development: Towards A Routing Model”. Fourth IEEE International Conference on Global Software Engineering, 2009. [10] E. Hess, and J. L. N. Audy, “FTSProc: a Process to Alleviate the Challenges of Projects that Use the Follow-the-Sun Strategy,” In: 7th International conference on Global Software Engineering (ICGSE), 2012, Porto Alegre, Brazil, in press. [11] C. Wohlin, “Experimentation in Software Engineering: An Introduction”, International Series in Software Engineering, Kluwer Print, 2000. [12] J. A. Espinosa, N. Nan, E. Carmel, "Do Gradations of Time Zone Separation Make a Difference in Performance? A First Laboratory Study," Global Software Engineering, 2007. ICGSE 2007. Second IEEE International Conference on , pp.12-22, 27- 30 Aug. 2007. [13] J. Kroll and J. L. N. Audy "Mapping Global Software Development Practices to Follow-the-Sun Process," In: 7th International conference on Global Software Engineering (ICGSE), 2012, Porto Alegre, Brazil., in press [14] R. M. Czekster, P. Fernandes, R. Prikladnicki, A. Sales, A. R. Santos, and T. Webber "Follow-The-Sun Methodology in a Stochastic Modeling Perspective". In 6th IEEE International Conference on Global Software Engineering (ICGSE): Methods and Tools for Project/ Architecture/Risk Management in Globally Distributed Software Development Projects (PARIS), pages 54–59, Helsinki, Finland, August 2011. [15] R. Phalnikar, V. S. Deshpande, S. D. Joshi, "Applying Agile Principles for Distributed Software Development," Advanced Computer Control, 2009. ICACC '09. International Conference on, pp.535-539, 22-24 Jan. 2009. [16] D. Šmite, N. B. .Moe, P. J.Ågerfalk, Agility Across Time and Space: Implementing Agile Methods in Global Software Projects. 1st Edition., 2010, XXXVI, 341 p. 37 illus. [17] A. Gupta, “Deriving mutual benefits from offshore outsourcing,” Communications of the ACM, v.52 n.6, 2009. [18] GTalk. Google talk. Available at http://www.google.com/talk/. [19] M. Höst, B. Regnell, B. and C. Wohlin. “Using Students as Subjects - A Comparative Study of Students and Professionals in Lead-Time Impact Assessment.” Empirical Software Engineering, Vol. 5, No. 3, 2000, pp. 201-214. 556
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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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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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Cloud Application Resource Mapping
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Fig. 1. UML Collaboration Diagram m
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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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scribed by the equation: where η i
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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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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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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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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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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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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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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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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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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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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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level); in UML/J2EE technology [11]
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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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III.BUSINESS RULES FRAMEWORK FOR KN
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A model introducing SOAs quality at
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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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equirements level, like EA-Miner [1
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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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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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Ontology-based Representation of Si
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I2Sim simulation model was transfor
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Automatic Generation of Architectur
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different from replacing it, we dec
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Using FCA-based Change Impact Analy
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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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Model & Method Feasibility Evaluati
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Structural Testing for Multithreade
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A Tiny Specification Metalanguage W
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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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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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[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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