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extracting relevant information contained in the code. Thus to build this ASTM model, a COBOL grammar must be used to parsing the legacy source code. The result of this parsing phase is then used to obtain the SASTM (Right part on Fig. 8). The obtained SASTM model contains specificities closely related to the COBOL programming language like MOVE, PERFORM, etc. In order to obtain more abstract elements, the transformation of this model into a GASTM one is required (not showed here). 4.2. Interfacing This second step aims at interfacing between ASTM and KDM by using our SMARTBRIDGE metamodel in order to progressively raise the abstraction level and also to iteratively enrich the target KDM model. This interfacing is illustrated in Figure 8. Figure 8. Interfacing example 4.3. Abstraction refining The third and last step of our modernization process aims at reaching a first abstraction level and at enriching the initial model. In order to accomplish this step, a true source code comprehension is necessary. In fact the MOVE instruction (see COBOL code sample) initializes the value Error-Message. This field represents the parameter. The KDM representation of the PERFORM statement is a CallableUnit call with a parameter value of ’sample error’. Figure 9 shows the KDM representation obtained using SMARTBRIDGE. 5. Related Works In this section, we study other works that address modernization issues through model-driven technologies. Logically, we pay a special attention to those focusing on the code level of legacy applications. In contrast with KDMcompliant approaches, approaches relying on proprietary metamodels, tailored for particular usages do exist. Figure 9. Abstraction refining example 5.1. KDM-uncompliant Reus et al. in [15] propose a MDA process for software migration where they parse the text of the original system and build a model of the abstract syntax tree. This model is then transformed into an intermediate langage dubbed GenericAST that can be translated into UML. In [6], the authors summarize the use and impact of the TGraph technology in Reverse Engineering. TGraphs [5] are directed graphs whose vertices and edges are typed, attributed, and ordered. In fact, representing source code as a typed graph can be rephrased as representing code as a model conforming to a metamodel. From this point of view, metamodels used by parsers are designed from scratch. Izquierdo and Molina developed the Gra2MoL approach [3], where a model extraction process is considered as a grammar-to-model transformation, so mappings between grammar elements and metamodel elements are explicitly specified. Beyond the technical aspects of the proposed transformation language, one may notice that the target metamodels are user-defined. Fleurey et al. describe in [9] a model-driven migration process in an industrial context. For that purpose, a tool suite for model manipulation is used as a basis for automating the migration. The reverse engineering step moves from a code model (output of the parsing) to a PIM, which is implemented by model transformations from a legacy language meta-model (e.g., COBOL) to a pivot metamodel. The pivot metamodel is called ANT and contains packages to represent data structures, actions, UIs and application navigation. 5.2. KDM-compliant In [14], Perez-Castillo et al. propose a technique that recovers code-to-data links in legacy systems based on relational databases and enable one to represent and manage these linkages throughout the entire reengineering pro- 523
cess. The proposal follows the ADM approach by leveraging KDM, especially the code package of the Program Elements Layer where SQL sentences have been modeled through a KDM extension. In this case, it is not an actual metamodel, it is a profile one instead. MoDisco (Model Discovery) [1] is the model extraction framework part of the Eclipse GMT project (www.eclipse.org/gmt). This framework is currently under development and provides a model managing infrastructure dedicated to the implementation of dedicated parsers (”discoverers” in MoDisco terminology). A KDM-based metamodel, a metamodel extension mechanism and a methodology for designing such extensions are also planned. 6. Conclusion ASTM and KDM complement each other in modeling software systems’ syntax and semantics. In this article, we propose to fill the gap between the two by introducing SMARTBRIDGE in order to remain in the scope of ADM and hence ensure the interoperability of the outputs of MDD reverse engineering activities. Gluing ASTM and KDM aims at overcoming the main flaw of a strict discretization of the modernization process, thus enabling roundtrip engineering. It also balances out the purely low-level representation of the legacy material supported by ASTM and the higher abstraction level supported by KDM. Hence, SMARTBRIDGE has been implemented within BLUAGE R○ (www.bluage.com) and has been proven to better represent code, while maintaining good architectural representation; rather than using KDM and ASTM separately. As such, we have demonstrated – in a tooling purpose – that SMARTBRIDGE supplies a practical answer to the traceability from end-to-end issue, along with knowledge propagation at every step. We are currently improving SMARTBRIDGE with additional features. Indeed, similarly to the glue ASTM-KDM, we plan to fill the gap with further OMG ADM metamodels like SPAP (Software Patterns Analysis Package), SMM (Software Metrics Metamodel), etc. These new relationships will make it possible to handle the different aspects of a legacy system as a cohesive whole. Acknowledgements This work has been funded by the European Commission through the REMICS project (www.remics.eu), contract number 257793, within the 7th Framework Programme. References [1] H. Bruneliere, J. Cabot, F. Jouault, and F. Madiot. Modisco: a generic and extensible framework for model driven reverse engineering. In Proceedings of the IEEE/ACM international conference on Automated software engineering, ASE ’10, pages 173–174, New York, NY, USA, 2010. ACM. [2] E. J. Chikofsky and J. H. Cross II. Reverse engineering and design recovery: A taxonomy. IEEE Softw., 7:13–17, January 1990. [3] J. Cnovas Izquierdo and J. Molina. A domain specific language for extracting models in software modernization. In R. Paige, A. Hartman, and A. Rensink, editors, Model Driven Architecture - Foundations and Applications, volume 5562 of Lecture Notes in Computer Science, pages 82–97. Springer Berlin / Heidelberg, 2009. [4] S. Ducasse and D. Pollet. Software architecture reconstruction: A process-oriented taxonomy. IEEE Trans. Softw. Eng., 35:573–591, July 2009. [5] J. Ebert and A. Franzke. A declarative approach to graph based modeling. In Proceedings of the 20th International Workshop on Graph-Theoretic Concepts in Computer Science, WG ’94, pages 38–50, London, UK, 1995. Springer- Verlag. [6] J. Ebert, V. Riediger, and A. Winter. Graph technology in reverse engineering: The tgraph approach. In Workshop Software Reengineering, pages 67–81, 2008. [7] L. Favre. Model Driven Architecture for Reverse Engineering Technologies: Strategic Directions and System Evolution. Premier Reference Source. Igi Global, 2010. [8] F.Barbier, G.Deltombe, O.Parisy, and K.Youbi. Model driven reverse engineering: Increasing legacy technology independence. In The 4th India Software Engineering Conference, Thiruvanantpuram, India, February 2011. CSI ed. [9] F. Fleurey, E. Breton, B. Baudry, A. Nicolas, and J.-M. Jézéquel. Model-driven engineering for software migration in a large industrial context. In MoDELS, pages 482–497, 2007. [10] I. Kurtev, J. Bézivin, and M. Aksit. Technological spaces: An initial appraisal. In CoopIS, DOA 2002 Federated Conferences, Industrial track, 2002. [11] A. S. F. B. Mohagheghi Parastoo, Berre Arne Jrgen and G. Benguria. Reuse and migration of legacy systems to interoperable cloud services - the remics project. In Proceedings of Mda4ServiceCloud’10 at the Sixth European Conference on Modelling Foundations and Applications, ECMFA ’10, June 2010. [12] OMG). Knowledge discovery metamodel - version 1.3. http://www.omg.org/spec/KDM/1.3, 2011. [13] OMG. Syntax tree metamodel - version 1.0. http://www.omg.org/spec/ASTM/1.0, 2011. [14] R. Perez-Castillo, I. G.-R. de Guzman, O. Avila-Garcia, and M. Piattini. On the use of adm to contextualize data on legacy source code for software modernization. Reverse Engineering, Working Conference on, 0:128–132, 2009. [15] T. Reus, H. Geers, and A. van Deursen. Harvesting software systems for mda-based reengineering. In ECMDA-FA, pages 213–225, 2006. [16] W. Ulrich. A status on omg architecture-driven modernization task force. In Proceedings EDOC Workshop on Model- Driven Evolution of Legacy Systems, Monterey, California, USA, 2004. IEEE Computer Society. 524
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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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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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320
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322
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A model introducing SOAs quality at
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there exist a hierarchical ranking
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Software as a Service: Undo Hernán
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operations consist in reinjection i
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stored. If event failure, external
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ensure users the trustworthiness of
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Patient Doctor Doctor Patient Direc
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going down at a time. This is a rea
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Decidability of Minimal Supports of
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A S 1 1 1 0 0 0 1 1 0 0 0 1 1
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satisfies R( D) R( C) S 1. Compa
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Automated Generation of Concurrent
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target of testing. A blackbox test
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sequence. Each t i θ i in the firi
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SAMAT -AToolforSoftware Architectur
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Figure 4. The SAM Hierarchical Mode
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High-level Petri net Place Place Ty
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verification and thus is often limi
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(r 1 r 2 ...r k ) 1 denotes Path [r
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Fig. 3. Example a 3-compound weakly
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364
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366
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368
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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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376
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378
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380
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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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Page 515 and 516: A Tiny Specification Metalanguage W
Page 517 and 518: C. Syntax Extensions The expressive
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Page 525 and 526: Figure 1. The process for engineeri
Page 527 and 528: Figure 4. The abstractions extracte
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Page 531 and 532: Algorithm 1: Algorithm to recursive
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Page 535 and 536: A. Pattern Reuse In patterns reuse
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Page 543 and 544: DC2AP Element and their Application
Page 545 and 546: 21. Consequences Example of use DC2
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Page 549 and 550: 2.3.1 Complementarity of MDD techno
Page 551: 3.2.2 EAggregateRelationship The EA
Page 555 and 556: II. OVERVIEW OF MODAL TRANSITION SY
Page 557 and 558: (1) V □ (A) ⊆ V □ (B), V ♦
Page 559 and 560: Suppose P = 〈S P , SP i , AI P ,
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Page 563 and 564: A. Step S1 - Define the Business Pr
Page 565 and 566: F. Step S6 - Monitor at Run-Time Th
Page 567 and 568: Figure 1, a metamodel defines an XM
Page 569 and 570: Figure 3 is a feature diagram that
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Page 573 and 574: Pub-T Tc Tc Sub-T Sub-T Figu
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Page 579 and 580: proposed in this work requires the
Page 581 and 582: FTS is an important research area,
Page 583 and 584: C. Dependent Variables and Measures
Page 585 and 586: D. Conclusion Validity Conclusion v
Page 587 and 588: evaluation of team productivity, qu
Page 589 and 590: Figure 3: Process Fragment Example
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Page 599 and 600: Investigating the use of Bayesian n
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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
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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