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Definition 8. Let Σ be the service defined by structure < Ε Σ , ο τ Σ , Φ > where Ε Σ Ε and ο τ Σ ο τ . Definition 9. Let εj1 εj2 εjn Χ = ο τ Φ ο τ Φ ... Φ ο τ be a composition of transformations which verifies εji C Cr ο τ : ε j ε j for all i :1...n. By algebraic C construction Χ : ε j ε Cr j . Proposition 3. The composition of transformations X ha s inverse and is bijective. εj1 εj2 Proof: Let be Χ = ο τ Φ ο τ Φ ... Φ ο εjn τ . For εji all i:1...n verifies ο τ has inverse by proposition 2. Let [ο εji τ ] -1 be the inverse transformation of ο εji τ , by usual algebraic properties [ο εji τ ] -1 is bijective. Then it is possible to compose a transformation X -1 = [ο εjn τ ] -1 Φ [ο εjn-1 τ ] -1 Φ ... Φ [ο εj1 τ ] -1 . The transformation X -1 is bijective by being composition of bijective transformations. Then transformation X -1 : ε Cr j ε C j exists and is the inverse of X. QED. Definition 10. Let UNDO be the X -1 transformation of X. IV. STRUCTURE OF UNDO FRAMEWORK In this section, we will describe our proposal for designing the undo pattern using SaaS to implement the replay of data. A. Undo Service Architecture Figure 2 represents the service Undo infrastructure, a highlevel abstraction of the architecture. Undo service has 3 modules, (a) Undo Business Layer, (b) Undo Application Layer (c) Undo Technology Layer. Undo Business Layer is responsible for creating, maintaining and deleting applications that will access the undo service. An a pplication that could access to se rvice must execute following steps: (a) creating application unique identifier, this should be attached to each message that is sent to the service, (b) creation of user profile identifier, this must be attached to each message that is sent to the service, once defined two identifiers, host application may immediately use undo service All these added to the header data set that can be invoked by the user for later retrieval, enable the service to h andle different applications at the same time, within an application users can manage their own recovery without interfering lists, plus each user can manage their own separate lists per interface; the service giving maximum flexibility for every application. Figure 2. Undo infrastructure B. Operation of Undo Framework Fig. 3 details process of send and get data to service, first we described undo receives data service from external system, at this point is where you start the process that ended with the injection of data re be invoked by the external system. In the External Layer, the user application generates an event that triggers an action likely to be overturned, this creates an Undo Service invocation, this is received by the service interface that is plotted on the Undo Abstract Layer, this action fires a set of processes: (i) Check current user session, this start with Validate Session and Profile, this process communicates with the Undo Application Layer, with f unction that processes Validate Undo Service Session and Profile. This service is based on two components responsible for validation and maintenance of active user sessions and profile´s user, Session component is responsible for validating whether the session with which you access the service is active, component Profile is responsible for validating invocation of the temporary storage. Both components communicate with lower-level layer called Layer Undo Technology, this is basic infrastructure for Undo service, which consists of a processing unit and data storage. (ii) After that, the validation process begins to check if host application has access to temporary storage, this process communicates with Validate Undo Data process, and it is responsible for validating the data to be stored, first validates that host application is active, if so, host application obtains credentials to use. If the process is successful the user is returned a successful update code, if an error occurs, it returns an error code also asynchronously, and with external system code decides if it generates exception or continues with the normal flow. Figure 3. Undo receives data service At last we described process which undo data service return stored temporarily to external system, this is where it describes the beginning of re injection data by the external system for the service provided. In the same way explained above, Layer External triggers an event that generates a r equest for data stored, this process is divided into two stages: (i) Charge of the validation of the application, which has the same activities as described in the process of Undo Send Data, (ii) Retrieve all values that have been stored for the tuple, application and interface. Return of this process to External Layer is the list sorted in reverse with all values stored, service provide option to request only the last value 331
stored. If event failure, external application receives an asynchronously error code. V. CONCLUSIONS In this paper we have proposed the design of an undo framework to b uild the undo functionality into any software application whatsoever. The most salient feature of this framework is the type of information it stores to be able to undo the user operations: input data instead of in-memory object states or commands executed by the system. This lessens the impact of building the framework into the target application a great deal. Building an Undo Service has some significant advantages with respect to Undo models presented, first of all the simplicity of inclusion in a host application under construction or existing, you can see in the proof of concept. Second the independence of service in relation to the host application allows the same architectural model to provide answers to different applications in di fferent domains. Construction of a service allows to Undo be a complex application, with possibility of include analysis for process improvement, as described in the next paragraph it is possible to detect patterns of invocation of Undo in different applications. Further work is going to bring: (a) creation of a precompiler, (8) automatic detection of fields to store, (c) extend the framework to other platforms. VI. REFERENCES 1. Ferre, X., Juristo, N., Moreno, A., Sanchez, I. 2003. A Software Architectural View of Usability Patterns. 2nd Workshop on Software and Usability Cross-Pollination (at INTERACT'03) Zurich (Switzerland) 2. Ferre, X; Juristo, N; and Moreno, A. 2004. Framework for Integrating Usability Practices into the Software Process. Madrid Polit. University. 3. Juristo, N; Moreno, A; Sanchez-Segura, M; Davis, A. 2005. Gathering Usability Information through Elicitation Patterns. 4. Abowd, G.; Dix, A. 1991. Giving UNDO attention. University of York. 5. Qin, X. y Sun, C. 2001. Efficient Recovery algorithm in Real-Time and Fault-Tolerant Collaborative Editing <strong>Systems</strong>. School of computing and Information Technology Griffith University Australia. 6. Bates, C. and Ryan, M. 2000. Method and system for UNDOing edits with selected portion of electronic documents. PN: 6.108.668 US. 7. Spring framework. http://www.springsource.org/. 8. Hibernate framework. http://www.hibernate.org/. 9. Baker, B. and Storisteanu, A. 2001. Text edit system with enhanced UNDO user interface. PN: 6.185.591 US. 10. Sun, C. 2000. Undo any operation at time in group editors. School of Computing and Information Technology, Griffith University Australia. 11. Chen, D; Sun, C. 2001. Undoing Any Operation in Collaborative Graphics Editing <strong>Systems</strong>. School of Computing and Information Technology, Griffith University Australia. 12. Yang, J; Gu, N; Wu, X. 2004. A Documento mark Based Method Supporting Group Undo. Department of Computing and Information Technology. Fudan University. 13. Brown, A; Patterson, D, 2003. Undo for Operators: Building an Undoable E-mail Store. University of California, Berkeley. EECS Computer Science Division. 14. Abrams, S. and Oppenheim, D. 2001. Method and apparatus for combining UNDO and redo contexts in a distributed access environment. PN: 6.192.378 US. 15. Berlage, T; Genau, A. 1993. From Undo to Multi-User Applications. German National Research Center for Computer Science. 16. Mancini, R., Dix, A., Levialdi, S. 1996. Reflections on UNDO. University of Rome. 17. Berlage. T. 1994. A selective UNDO Mechanism for Graphical User Interfaces Based On command Objects. German National Research Center for Computer Sc. 18. Burke, S. 2007. UNDO infrastructure. PN: 7.207.034 US. 19. Korenshtein, R. 2003. Selective UNDO. PN: 6.523.134 US. 20. Washizaki, H; Fukazawa, Y. 2002. Dynamic Hierarchical Undo Facility in a Fine-Grained Component Environment. Department of InformaTION AND Computer Science, Waswda University. Japan. 21. Edwards, W; Mynatt, E. 1998. Timewarp: Techniques for Autonomous Collaboration. Xerox Palo Alto Research Center. 22. Dix, A; Mancini, R; Levialdi, S. 1997. The cube – extending systems for undo. School of Computing, Staffordshire University. UK. 23. Edwards, W: Igarashi, T; La Marca, Anthony; Mynatt, E. 2000. A Temporal Model for Multi-Level Undo and Redo. 24. O´Brain, J; Shapiro, M. 2004. Undo for anyone, anywhere, anytime. Microsoft Res.. 25. Buschmann, F; Meunier, R; Rohnert, H; Sommerlad, P; Stal, M. 1996. Pattern-Oriented Software Architecture: A System Of Patterns. John Wiley & Sons. 26. Fayad, M.; Shumidt, D. 1997. Object Oriented Application Frameworks. Comunications of the ACM, 40(10) pp 32-38. 27. Meshorer, T. 1998. Add an undo/redo function to you Java app with Swing. JavaWord, June, IDG Communications. 28. Shinnar, A; Tarditi, D; P lesko, M; Steensgaard, B. 2004. Integrating support for undo with exception handling. Microsoft Research. 29. Keane, P. and Mitchell, K. 1996. Method of and system for providing application programs with an UNDO/redo function. PN:5.481.710 US. 30. Nakajima, S., Wash, B. 1997. Multiple level UNDO/redo mechanism. PN: 5.659.747 US. 31. Li, C. 2006. UNDO/redo algorithm for a computer program. PN: 7.003.695 US. 32. Martinez, A. and Rhan, M. 2000. Figureical UNDO/redo manager and method. PN: 6.111.575 US. 33. Gamma, E., R. Helm, R. Johnson, and J. Vlissides. 1994. Design Patterns: Elements of Reusable Object-Oriented Software, Addison- Wesley. 332
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SEKE San Francisco Bay July 1-3 201
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Copyright © 2012 by Knowledge Syst
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The 24 th International Conference
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Zhenyu Chen, Nanjing University, Ch
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Riccardo Martoglia, University of M
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Poster/Demo Sessions Co-Chairs Fars
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Evaluating the Cost-Effectiveness o
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Program Understanding Evaluating Op
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An Empirical Study of Execution-Dat
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Computer Forensics: Toward the Cons
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Modeling Bridging KDM and ASTM for
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A Mapping Study on Software Product
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A proposal for the improvement of t
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Panel on Future Trends of Software
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een available for analysis. Social
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The rest of this paper is orga nize
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Now we assume is given, we first pr
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“iphone” and “blackberry” a
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nique estimate the impact set based
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Figure 1. An example to illustrate
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trategy, the results should support
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Figure 1. System Architecture would
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Figure 2. Sequence Diagram of Train
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deployed on Android phone and runs
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the ontology information is only us
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Both kinds of axioms lead to an inh
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engineer and achieving requirements
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elicitation and the act ivities con
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Step 2. Step 3. 1.2. Use of Procedu
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Figure 6.b. Task: Cognitive Analysi
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original User’s Discourse / Speec
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II. RELATED WORK Business processes
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test, we did not find any significa
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For the indicator “requirements a
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the model checkers SPIN [8] and NuS
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allow for the presentation of syste
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the requirement or design phases sh
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using the CR estimates against the
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Data Set TABLE II. DATA SETS Artifa
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Figure 4. Relative Error in the Est
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Take basic requirements from user a
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sent Semantic Web Ontologies. It co
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The rest of the paper is organized
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and the risk level. Existing assess
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diagram could give developers an in
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Figure 2. Co
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• Sensor(Lexical): at the top and
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An Overview of the RSLingo Approach
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Figure 2. Overview of the RSLingo a
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DETECTING EMERGENT BEHAVIOR IN DIST
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Definition 2: For a set of MSCs M,
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Stability of Filter-Based Feature S
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Algorithm 1: Threshold-Based Featur
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MI KS GM AUC PRC S2N RUS35 RUS50 RO
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80
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82
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84
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86
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Cloud Application Resource Mapping
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Fig. 1. UML Collaboration Diagram m
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Fig. 2. UML Activity Diagram modeli
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An Empirical Study of Software Metr
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TABLE I SOFTWARE DATASETS CHARACTER
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1) Classifiers: In this study, soft
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Progressive Clustering with Learned
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IV. DATA SOURCES We have been worki
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meta-data we assign to indicate sig
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Multi-Objective Optimization of Fuz
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uilding FNNs that satisfy different
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VI. EXPERIMENTAL RESULTS The presen
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Generating Performance Test Scripts
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test duration(s) for the scenario(s
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which are composed by the name and
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A Catalog of Patterns for Concept L
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assisted solution for lattice analy
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The complete lattice of the class s
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Client-Side Rendering Mechanism: A
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JavaScript code directly within the
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Fig. 6. CPU usage percentage of pre
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may introduce the extra learning cu
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[9] but no validation is provided i
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One of the st udents from Group B t
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scribed by the equation: where η i
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4 Model Checking DPN We use HyTech
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Recommender systems are usually cla
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such as one week and gradually dete
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•
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146
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Enforcing Contracts for Aspect-orie
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Table 1. Behavioral Rules in LSD [9
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1 public privileged aspect Update {
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Towards More Generic Aspect-Oriente
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vals must be denoted by some notion
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Aspect-Orientation in the Developme
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Table II SELECTED PRIMARY STUDIES #
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Evaluating Open Source Reverse Engi
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Table 1. Candidate reverse engineer
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Some tools may perform the needed f
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Coordination Model to Support Visua
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this CMV approach we employee three
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colored according to the Gradient T
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Improving Program Comprehension in
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The method used to support and eval
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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
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An Approach for Software Component
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properties. In particular, the foll
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Conference Dataset Anatomy Dataset
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equipment maintenance node may cont
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Online Anomaly Detection for Compon
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B. Monitoring Static Method Invocat
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undle which is responsible for pars
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An Exploratory Study of One-Use and
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Figure 1. Mindmap of Reuse Design P
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subject also caused outliers for re
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Choosing licenses in free open sour
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The important aspect is to provide
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that holds a model of each software
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Online Probability Application Char
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TABLE II THE PARAMETERS FOR THE HAR
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[10] N. Gunther, “Hit-and-run tac
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Section 5 d iscusses some related w
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model and the analysis results. The
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tree view of UML model, as show in
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II. BACKGROUND In this section, we
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Algorithm 2 MarkStatements function
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(a) printtok (b) printtok2 (c) sche
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Gupta extended HGS and proposed the
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manage intellectual knowledge with
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D. Manage Training When any trainin
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TABLE II. COMPARISON BETWEEN THE EX
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B. Trace Semantics of DAP Fig. 1. A
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∀ [DR ′ ] []gen [; ] [D]□ [;
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transformation idea in MDA [13] . T
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MappingRule MappingTGtoHP { [Rule I
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executing a continuous transition,
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protocol of DATS we used is properl
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in Boolean search and reasoning has
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to be broken by expelling one of it
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where, among other things: 1. The t
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A never-ending language learner cal
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4. i 2 Learning(DEC, mex) via Bias
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4.4. An Illustrative Example Now le
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Evolutionary Learning and Fuzzy Log
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In addition to the rules, the knowl
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As described in the previous sectio
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the ontology hierarchy; learning a
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three different learning techniques
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TABLE IX. USING SVM FOR LEARNING tr
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Siemens set contains seven C progra
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The program tcas is not included be
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esults. To be more specific, they w
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According to IEEE [10], regression
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(a) Volatility (b) Complexity (c) R
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It mimics the test engineer knowled
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II. RESOURCE-ORIENTED WORKFLOW MODE
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eventually triggers a co mmon task.
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Input: A resource oriented workflow
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access control exists, the action i
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(ACCDA), is customized to address a
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ICrudSchema and shown in Figure 3.
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Connectors for Secure Software Arch
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Non-repudiation security service pr
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anAsynchronousCustomer InterfaceCon
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How Social Network APIs Have Ended
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OSN Social Feed Unique Features Con
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users can control the reach of thei
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Computer Forensics: Toward the Cons
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In 2009, Cohen et al. in [4] have o
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dcterms:contributor, dcterms:creato
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A Holistic Approach to Software Tra
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Figure 1. Figure 1 shows an overvie
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B. Case study in a proprietary proj
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Pointcut Design with AODL Saqib iqb
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system attributes, methods and exec
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Feature modeling and Verification b
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hasOptionalPart hasPart hasMandato
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A Context Ontology Model for Pervas
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Figure 1. UML view of the model. Th
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Figure 3. Architecture for deliveri
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Ontology-based Representation of Si
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Figure 2. Upper Ontology fragment f
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I2Sim simulation model was transfor
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An ontology-based approach for stor
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fying the equivalences between conc
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• Homonymy conflicts: occur when
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Automatic Generation of Architectur
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we dealt w ith the verticals rules.
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Towards Architectural Evolution thr
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different from replacing it, we dec
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Using FCA-based Change Impact Analy
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Table 2. Impact set of C1,C2,C5 cha
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Figure 3. The PF and PTS results fo
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Forecasting Fault Events in Power D
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which the airport data did not cont
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classified. Recall is the probabili
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Testing Interoperability Security P
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Figure 2. Overview of the Test Gene
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Objective Figure 4. Testing
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A New Approach to Evaluate Path Fea
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k v ( df i ) 0 , it means that the
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Model & Method Feasibility Evaluati
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Structural Testing for Multithreade
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adequate to another criterion. This
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480
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482
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484
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A Tiny Specification Metalanguage W
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C. Syntax Extensions The expressive
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however, does not allow variables f
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derivation dependencies. The third
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model extension as proposed in [6].
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Figure 1. The process for engineeri
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Figure 4. The abstractions extracte
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way to detect something which could
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Algorithm 1: Algorithm to recursive
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obtain the input of experts on desi
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A. Pattern Reuse In patterns reuse
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IV. SCATTER In order to enhance pat
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These latter would be clustered acc
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described or even r etrieved, thus
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DC2AP Element and their Application
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21. Consequences Example of use DC2
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Legacy2Cloud logic. In this scope,
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2.3.1 Complementarity of MDD techno
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3.2.2 EAggregateRelationship The EA
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cess. The proposal follows the ADM
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II. OVERVIEW OF MODAL TRANSITION SY
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(1) V □ (A) ⊆ V □ (B), V ♦
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Suppose P = 〈S P , SP i , AI P ,
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Adaptive software should basically
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A. Step S1 - Define the Business Pr
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F. Step S6 - Monitor at Run-Time Th
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Figure 1, a metamodel defines an XM
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Figure 3 is a feature diagram that
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ecause the system of C-net model is
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Pub-T Tc Tc Sub-T Sub-T Figu
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Visual Studio Achievements, a Visua
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the project. This achievement has t
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proposed in this work requires the
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FTS is an important research area,
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C. Dependent Variables and Measures
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D. Conclusion Validity Conclusion v
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evaluation of team productivity, qu
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Figure 3: Process Fragment Example
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complexity issues traditionally inv
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Fig. 1: Swing Framework Class Diagr
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ize:Class”, with an empty precond
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Fig. 11: Overwritten Method Fig. 12
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Investigating the use of Bayesian n
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process for the network. Thus, this
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Reuse of Experiences Applied to Req
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Step 1: To start the process, the u
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Specification of Safety Critical Sy
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current belief of agents in order t
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Using the Results from a Systematic
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obtained from the categorization of
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that automatically presents the usa
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Improving a Web Usability Inspectio
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Following the experimental methodol
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About the category “Improvements
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Identification Guidelines for the D
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created according to the preview re
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In our analysis, the “Government
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In the following, we present the re
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USE SCENARIO The application Vuelos
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[8] Mayhew, D., “The Usability En
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Interaction State Set Structure Dat
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B. Component Model Fig. 2 show s th
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As a continuation of our research w
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PROMETHEE methods [3] belong to a f
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comparison rule, a value of 1/9 is
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TABLE V: Supermatrix for the exampl
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for knowledge sharing. Based on str
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y applying hash functions to its su
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Empirical Validation of Variability
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III. EXPERIMENTAL STUDY In this sec
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Table III SPEARMAN’S CORRELATION
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A Mapping Study on Software Product
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most relevant sources in software e
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TABLE IV TOOL’S CLASSIFICATION AC
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[2] P. Clements and L. Northrop, So
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and future works. II. BACKGROUND ON
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System Advanced Standard Module A M
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Algorithm 1: Backtracking for MIN-A
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outcomes from such research fields,
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TABLE IV. CONTINUED FROM PREVIOUS P
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contribution of each study was made
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assets, the plugin approach can be
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Figure 3: PlugSPL Feature Model Edi
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contributions from the several acto
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o Guideline 6.4: Softgoal dependenc
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descriptions must also b e configur
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It means that throughout the develo
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B. Instrumentation The experiment w
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• External Validity: W e have con
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II. THE PROPOSED TAXONOMY The taxon
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sub-dimension is categorized as, co
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A Proposal of Reference Architectur
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Figure. 1. Reference Architecture M
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A Variability Management Method for
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C. Correctness of configuration fil
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means patterns which are shown in s
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Tool Support for Anomaly Detection
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Figure 1. System overview of the SD
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Once the datasets were evaluated us
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Reconfiguration of Robot Applicatio
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data dependencies required for keep
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Spacemaker: Practical Formal Synthe
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Fig. 1. The ecommerce object model.
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Fig. 3. Mapping diagram for Figure
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A formal support for incremental be
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machine may be empty which should l
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software, based on revised requirem
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A Process-Based Approach to Improvi
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Appropriate processes m ust support
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Figure 2. Reordered layers of the k
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Automatic Acquisition of isA Relati
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III. VALIDATING THE DIMENSION HEADE
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The table title and the dimension s
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A light weight alternative for OLAP
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i.d. subsets of cubes focused on se
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Match production rules Enterprise S
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A Tool for Visualization of a Knowl
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other ontology visualization tools
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shown at Figure 5. Objectives are s
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Rendering UML Activity Diagrams as
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a = O1 → a → O2 b = O2 → b
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ecordProblem → A → reproducePro
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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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