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The Mann-Whitney U test of the qualitative data (survey responses) revealed that the mean difference in the responses to all the questions, for all mechanism, were statistically significant. Table VI shows the mean increases for all the mechanisms. We infer from these data that most mechanisms more than double, and progress feedback almost triples, user satisfaction. This means that users are on average twice as satisfied with the applications that provide usability mechanisms. TABLE III.RESULTS FOR THE IMPACT OF EACH MECHANISM ON THE EFFICIENCY ATTRIBUTE. Mechanism Increase (%) Global Undo * 46.98 Progress Feedback * 34.15 Structure Text Entry * 62.7 Go Back 14 *Statistically significant. TABLE IV.RESULTS FOR THE IMPACT OF EACH MECHANISM ON THE RELATIVE EFFICIENCY ATTRIBUTE. Mechanism TG relative CG relative Increase (%) efficiency efficiency Global Undo 87.3% # Progres Feed.* 100% 40% 60 Structured TE * 86% 25% 61 Go Back 72% 68% 4 * Statistically significant # This system implementation is unable to undo an operation unless the mechanism has been enabled TABLEV. Mechanism RESULTS FOR IMPACT ON EFFECTIVENESS ATTRIBUTE CG users that complete the task TG users that complete the task Increase (%) Globla Undo * 60.9 % 95.7 % 35.7 Progress Feedback 95.7 % 100 % 4.3 Structure Text Entry* 86.96 % 4.4 % 82.56 Go Back * 70 % 18 % 52 TABLE VI. *Statistically significant. RESULTS FOR THE IMPACT OF EACH MECHANISM ON THE SATISFACTION ATTRIBUTE MECHANISM MEAN INCREASE (%) Global Undo* 82.28 Progress Feedback* 294.08 Structured Text Entry* 251.50 Go Back* 205.75 *Statistically significant. III. CONCLUSIONS The conclusions of this paper refer to the results of the experiment on the EasyFlight application, as they are much more reliable than the results for the EasyTheatre application. The findings from the results analysis were: • The structured text entry mechanism was statistically significant in all cases. It also had a high impact, over 60%, on all usability attributes. • The progress feedback mechanism did not have a statistically significant impact on the effectiveness mechanism. The reason is that users always manage to complete the tasks, irrespective how much information they have on the progress of the task. • Note that control group users cannot complete the task if global undo is disabled. On this ground, we cannot calculate that relative efficiency of this mechanism. • The go back mechanism was not statistically significant for calculating the efficiency and relative efficiency attributes. A possible reason is that the browser go back functionality was enabled. This will be corrected in future experiments. We intend to continue testing the EasyFlight tool for other mechanisms: system status feedback, abort, cancel, warning and help. Note that the two software applications used in this experiment have similar features. Both are e-commerce transaction systems. Obviously, we have to check the results of this research on other software application types in order to generalize the results. To do this, we have now developed an event-driven home automation application which we will use to run a similar experiment in the very near future. REFERENCES [1] “ISO/IEC Std. 9126-1: Software engineering – Product quality,” 2001. [2] “ISO/IEC Std. 9241-11: Ergonomic requirements for office work with visual display terminals. Part11: "Guidance on Usability",” 1998. [3] “ISO/IEC Std. 25010-3: Systems and software engineering: Software product quality and system quality in use models,” 2009. [4] Cysneros, L.; Wemeck, V.; Kushniruk, A., “Reusable Knowledge for Satisficing Usability Requirements”. Proceedings of the 2005 13 th IEEE International Conference on Requirements Engineering. [5] Juristo, N.; Moreno, A.M.; Sanchez-Segura,M., “Analysing the impact of usability on Software Design” Journal of Systems and Software, Vol. 80,Issue 9,2007,pp.1506-1516. [6] Bass, L.; Bonnie, J.; Kates, J., “Achieving Usability Through Software Architecture”. Technical Report MU/SEI-2001-TR-005 ESC-TR-2001-005, (2001) [7] Juristo, N.; Moreno, A.M.; Sanchez-Segura M., “Guidelines for eliciting usability Functionalities”. IEEE Transactions on Software Engineering, Vol. 33, N°. 11, November 2007, pp. 744-758. 603
[8] Mayhew, D., “The Usability Engineering Lifecycle” Morgan Kaufman Publishers Inc. 1999. [9] Nielsen, J., ”Usability Engineering” Morgan Kaufman Publishers Inc. 1993. [10] Radven, S.;Graham, J., “Evaluating Usability of Human Computer Interfaces: A Practical Method” Ellis Horwood Limited 1989. [11] Rosson, M; Carroll, J., ”Usability Engineering” Morgan Kaufman Publishers Inc. 2002. [12] Florian B., Solarte O.; Reyes,J., “Propuestsa para Incorporar Evaluación y Pruebas de Usabilidad dentro de un Proceso de Desarrollo de Software” Revista EIA,ISSN 1794-1237 Número13,p.123-141, Julio 2010. [13] Macleod M.; Rengger R., “The Development of DRUM : A Software Tool for Video-assisted Usability Evaluation,” Methods, People and Computers VIII: proceedings of HCI 93, Loughborough, p. 293, 1994. [14] Ivory,M.; Hearst M., “The state of the art in automating usability evaluation of user interfaces,” ACM Computing Surveys, vol. 33, pp. 470–516, Dec. 2001. [15] National Institute of Standards and Technology, “NIST Web Metrics Testbed.” Online. Available from: http:// zing.ncsl.nist.gov/WebTools/. [Accessed: February 2, 2012], 2005. [16] UsableNet Inc., “Lift Machine.” Online. Available from: http://lfd.usablenet.com/usablenet_liftmachine.html. [Accessed: February 2, 2012]. [17] Clearleft Ltd “Silverback 2.0 - Guerrilla usability testing software for designers and developers.” Online. Available from: http://silverbackapp.com/. [Accessed: January 6, 2012], 2011. [18] Microsoft Corporation, “Expression Encoder 4 Pro.” Online. Available from: http://www.microsoft.com/expression/products/ EncoderPro_Overview.aspx. [Accessed: 06 January 2012], 2010. [19] Techsmith Corporation, “Morae. Usability testing and market research software”. Online. Available from:http:// www.techsmith.com/morae.asp. [Accessed: 06 January 2012]., 2011. [20] Aveledo, M., Curtino, D., De la Rosa, A.; Moreno,A., “Avoiding laboratories to collect usability data: two software applications”. Accepted in CISTI 2012. [21] Frøkjær, E., Hertzum, M.; Hornbæk, K, “Measuring usability: are effectiveness, efficiency, and satisfaction really correlated?,” in Proceedings of the SIGCHI conference on Human factors in computing systems, CHI ’00, (New York, NY, USA), pp. 345– 352, ACM, 2000 [22] Seffah,A.,Donyaee, M., Kline,R.; Padda, H., “Usability measurement and metrics: A consolidated model,” Software Quality Journal, vol. 14, pp. 159–178, June 2006. [23] Sauro,J.; Kindlund, E., “A Method to Standardize Usability Metrics Into a Single Score” CHI 05 Proceedings of the SIGHI. Available from: www.measuringusability.com/.../p482- sauro.pdf [Accessed : February 27, 2012] , 2005. [24] Bevan, N.; Macleod, M., “Usability measurement in context”. Behaviour and Information Technology 13,132-145, 1994. [25] Scriven M., “Evaluation Thesaurus” 4th ed. Newbury Park, C.A.. Sage Publications Inc. ,1991. [26] De la Rosa, A., “TeatroFácil.” Online. Available from: http:// www.grise.upm.es/tf [Accessed: February, 28 2012], 2009 [27] Brooke, J., “A quick and dirty usability scale” Available from www.itu.dk/courses/U/E2005/litteratur/sus.pdf [ Accessed: February , 28 2012],1996 [28] Pérez, C., “Técnica de análisis de datos con SPSS” Pearson Prentice Hall, 2009 [29] Nadim,N., “The Mann-Whitney U: A test for assessing whether two independent samples come from de same distribution” Tutorials in Quantitative methods for psychology 2008, vol. 4(1) pp. 13-20. [30] Curtino, D., “VuelosFácil”, Online Available from: http:// www.fingflights.com [Accessed : March 6, 2012], 2011. [31] Curtino, D., “Usability Mechanisms and Their Impact on Usability Attributes: A First Advance ”, Online Avaible from: http://www.fingflights.com/MasterThesis.pdf [Accesed : March 11, 2012], 2011. 604
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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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Cloud Application Resource Mapping
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Fig. 1. UML Collaboration Diagram m
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Fig. 2. UML Activity Diagram modeli
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An Empirical Study of Software Metr
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TABLE I SOFTWARE DATASETS CHARACTER
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1) Classifiers: In this study, soft
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Progressive Clustering with Learned
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IV. DATA SOURCES We have been worki
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meta-data we assign to indicate sig
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Multi-Objective Optimization of Fuz
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uilding FNNs that satisfy different
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VI. EXPERIMENTAL RESULTS The presen
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Generating Performance Test Scripts
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test duration(s) for the scenario(s
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which are composed by the name and
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A Catalog of Patterns for Concept L
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assisted solution for lattice analy
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The complete lattice of the class s
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Client-Side Rendering Mechanism: A
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JavaScript code directly within the
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Fig. 6. CPU usage percentage of pre
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may introduce the extra learning cu
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[9] but no validation is provided i
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One of the st udents from Group B t
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scribed by the equation: where η i
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4 Model Checking DPN We use HyTech
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Recommender systems are usually cla
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such as one week and gradually dete
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•
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146
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Enforcing Contracts for Aspect-orie
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Table 1. Behavioral Rules in LSD [9
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1 public privileged aspect Update {
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Towards More Generic Aspect-Oriente
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vals must be denoted by some notion
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Aspect-Orientation in the Developme
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Table II SELECTED PRIMARY STUDIES #
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Evaluating Open Source Reverse Engi
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Table 1. Candidate reverse engineer
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Some tools may perform the needed f
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Coordination Model to Support Visua
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this CMV approach we employee three
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colored according to the Gradient T
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Improving Program Comprehension in
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The method used to support and eval
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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
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An Approach for Software Component
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properties. In particular, the foll
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Conference Dataset Anatomy Dataset
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equipment maintenance node may cont
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Online Anomaly Detection for Compon
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B. Monitoring Static Method Invocat
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undle which is responsible for pars
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An Exploratory Study of One-Use and
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Figure 1. Mindmap of Reuse Design P
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subject also caused outliers for re
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Choosing licenses in free open sour
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The important aspect is to provide
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that holds a model of each software
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Online Probability Application Char
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TABLE II THE PARAMETERS FOR THE HAR
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[10] N. Gunther, “Hit-and-run tac
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Section 5 d iscusses some related w
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model and the analysis results. The
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tree view of UML model, as show in
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II. BACKGROUND In this section, we
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Algorithm 2 MarkStatements function
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(a) printtok (b) printtok2 (c) sche
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Gupta extended HGS and proposed the
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manage intellectual knowledge with
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D. Manage Training When any trainin
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TABLE II. COMPARISON BETWEEN THE EX
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B. Trace Semantics of DAP Fig. 1. A
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∀ [DR ′ ] []gen [; ] [D]□ [;
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transformation idea in MDA [13] . T
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MappingRule MappingTGtoHP { [Rule I
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executing a continuous transition,
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protocol of DATS we used is properl
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in Boolean search and reasoning has
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to be broken by expelling one of it
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where, among other things: 1. The t
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A never-ending language learner cal
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4. i 2 Learning(DEC, mex) via Bias
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4.4. An Illustrative Example Now le
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Evolutionary Learning and Fuzzy Log
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In addition to the rules, the knowl
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As described in the previous sectio
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the ontology hierarchy; learning a
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three different learning techniques
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TABLE IX. USING SVM FOR LEARNING tr
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Siemens set contains seven C progra
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The program tcas is not included be
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esults. To be more specific, they w
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According to IEEE [10], regression
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(a) Volatility (b) Complexity (c) R
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It mimics the test engineer knowled
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integrate the test results. • Aut
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No tify the cloud controller to get
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of execution-data classification. T
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Figure 1. Boxplots of average AUC r
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anches is more applicable than the
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To validate the proposed approach,
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contains broadcast of Subject to Ob
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S 1a=1; S 2b=5; cobegin { S 3read
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.
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298
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A functionality is the ability of a
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level); in UML/J2EE technology [11]
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• to provide a unified catalogue
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flexibility they provide to model s
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complete in zero-time, and thus hav
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figure assumes the experiment with
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could be a practical way of dealing
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and (iv) the reduced amount of line
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III.BUSINESS RULES FRAMEWORK FOR KN
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explanation for why the program wan
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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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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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Page 631: USE SCENARIO The application Vuelos
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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
Page 709 and 710:
Figure 1. System overview of the SD
Page 711 and 712:
Once the datasets were evaluated us
Page 713 and 714:
Reconfiguration of Robot Applicatio
Page 715 and 716:
data dependencies required for keep
Page 717 and 718:
Spacemaker: Practical Formal Synthe
Page 719 and 720:
Fig. 1. The ecommerce object model.
Page 721 and 722:
Fig. 3. Mapping diagram for Figure
Page 723 and 724:
A formal support for incremental be
Page 725 and 726:
machine may be empty which should l
Page 727 and 728:
software, based on revised requirem
Page 729 and 730:
A Process-Based Approach to Improvi
Page 731 and 732:
Appropriate processes m ust support
Page 733 and 734:
Figure 2. Reordered layers of the k
Page 735 and 736:
Automatic Acquisition of isA Relati
Page 737 and 738:
III. VALIDATING THE DIMENSION HEADE
Page 739 and 740:
The table title and the dimension s
Page 741 and 742:
A light weight alternative for OLAP
Page 743 and 744:
i.d. subsets of cubes focused on se
Page 745 and 746:
Match production rules Enterprise S
Page 747 and 748:
A Tool for Visualization of a Knowl
Page 749 and 750:
other ontology visualization tools
Page 751 and 752:
shown at Figure 5. Objectives are s
Page 753 and 754:
Rendering UML Activity Diagrams as
Page 755 and 756:
a = O1 → a → O2 b = O2 → b
Page 757 and 758:
ecordProblem → A → reproducePro
Page 759 and 760:
umlTUowl - A Both Generic and Vendo
Page 761 and 762:
The tool’s ability to deal with S
Page 763 and 764:
Elem. UML Example D 12 Harmonizing
Page 765 and 766:
4) Associations: In the first test
Page 767 and 768:
III. GRAPH REPRESENTATION OF CLASS
Page 769 and 770:
as an add-in to popular UML m odeli
Page 771 and 772:
742
Page 773 and 774:
744
Page 775 and 776:
746
Page 777 and 778:
her necessities. However, the progr
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Figure 3. Global Log. of terms. The
Page 781 and 782:
two stages. The first stage is focu
Page 783 and 784:
754
Page 785 and 786:
756
Page 787 and 788:
758
Page 789 and 790:
With the GDUC approach, we can mode
Page 791 and 792:
Figure 6. Three proposals containin
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764
Page 795 and 796:
766
Page 797 and 798:
Proactive Two Way Mobile Advertisem
Page 799 and 800:
information. If more than one adver
Page 801 and 802:
Users can al so publish adv ertisem
Page 803 and 804:
The COIN Platform: Supporting the M
Page 805 and 806:
A-3
Page 807 and 808:
Checking Contracts for AOP using XP
Page 809 and 810:
Maicon B. da Silveira, 112 Aldo Dag
Page 811 and 812:
Seyedehmehrnaz Mireslami, 70 Takao
Page 813 and 814:
Wei Zhang, 422 Wenbo Zhang, 188 Zhi
Page 815 and 816:
Desmond Greer Eric Gregoire Christi
Page 817 and 818:
Poster/Demo Presenter’s Index A A
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SEKE 2012 Proceedings - Knowledge Systems Institute

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