SEKE 2012 Proceedings - Knowledge Systems Institute

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latter deal with links between the layers that belong to the KDM metamodel itself. Building SMARTBRIDGE has led us to focus on three important features: 1. interfacing KDM and ASTM via joint points 2. ensuring navigability between them 3. mapping the different KDM layers: code, data and user interface 3.1. Interfacing The linkage between the low level code representation allowed by ASTM and the more abstract level allowed by KDM is not natively provided. To overcome this issue, SMARTBRIDGE interposes a number of meta-classes that are showed in table 1. ASTM SMARTBRIDGE KDM TypeDefinition EDataType DataType DataDefinition EDataElement DataElement TypeDeclaration EDataType DataType AggregateTypeDefinition EDataType RecordType AggregateTypeDeclaration EDataType RecordType FunctionDeclaration EControlElement CallableUnit FunctionDefinition EControlElement CallableUnit VariableDeclaration EDataElement StorableUnit VariableDefinition EDataElement StorableUnit Statement EActionElement ActionElement Table 1. Meta-classes for interfacing ASTM and KDM Achieving this interfacing calls for an extension point. It turns out that such an extension point was provided in the KDM specification by way of the meta-class CodeElement which belongs to Code package. Figure 3 shows how SMARTBRIDGE exploits this extension point in order to introduce the required metaclasses to bridge toward ASTM. Based on the KDM CodeElement meta-class, SMARTBRIDGE defines several meta-classes which are sub-classes of EElement. These metaclasses are ECodeElement, EActionElement, EDataElement, EControlElement and EDatatype. Figure 4 presents an interfacing example between an ASTM Statement and its KDM corresponding representation. 3.2. Navigability We decided to provide a bidirectional navigation capability between KDM and ASTM in order to be able to ob- Figure 3. CodeElement extension point Figure 4. EActionElement meta-class tain an ASTM code representation from an abstract code element representation, and conversely. The navigability implemented in SMARTBRIDGE is inspired by the relationship mechanism used in the KDM specification [12]. The KDM code package provides a natural extension point for this relationship mechanism through the CodeRelationship meta-class (Fig. 5). SMARTBRIDGE specializes this extension point in two metaclasses: ERelationship and EAggregateRelationship. 3.2.1 ERelationship The ERelationship meta-class defines the navigability between KDM meta-classes and the SMARTBRIDGE metaclasses. An ERelationship instance is used in order to navigate from a KDM CodeElement to a SMART- BRIDGE EElement and vice versa. This relationship is exclusively used for a one-to-one relationship, like a function representation (ASTM FunctionDefinition), which corresponds exactly to one abstract representation (KDM CallableUnit). 521
3.2.2 EAggregateRelationship The EAggregateRelationship meta-class defines the navigability one-to-many between a KDM element and many SMARTBRIDGE elements. This one-to-many relationship is useful in order to represent KDM abstract elements (like KDM ActionElement) using many concrete ASTM ones (like Statement metatype). Figure 6. Mapping Model Figure 5. CodeRelationship extension point 3.3. Mapping The need to provide a mapping between the type definition contained in the legacy source code and the data definition in a database or in the user interface is extremely strong. The KDM meta-model does not provide the concept of specific mapping between the different layers: code, data and user interface. For this reason SMARTBRIDGE introduces this lacking concept through a new package: the Mapping package. It defines the link between the source code and the UI from one side and the source code and the data from the other side. The package introduces a new KDM code model called MappingModel (c.f. Figure 6). This model contains the existing mapping set representing the data structures, the UI and the source code. SMARTBRIDGE includes the mapping concept through the abstract meta-class MappingElement. Thereby two concrete meta-classes are defined to map the KDM code package element with the KDM data package element DataMapping and the KDM ui package element UIMapping (Fig. 7). Thanks to these three important features, i.e. navigability, interfacing and mapping, SMARTBRIDGE fills the gap between the KDM and the ASTM metamodels. This is essential to reach a suitable level of abstraction which enables a better understanding of the legacy source code, and hence an easier modernization activity. Figure 7. Focus on UI Mapping 4. Working example This section provides an illustration of SMARTBRIDGE for the modernization of the COBOL legacy code. This illustration especialy focuses on the benefits of interfacing KDM and ASTM. For the sake of simplicity, the following illustrations are based on the tiny COBOL code snippet below: MOVE "sample error" Error-Message PERFORM FERROR The modernization process is based on the three following steps: 1. Text to Model transformation (aka. Injection) 2. Interfacing 3. Abstraction refining 4.1. Injection The first step in the modernization process as discussed in Section 2.3.2, is to obtain an abstract syntax tree conforming to the ASTM metamodel. This step is crucial in 522
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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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Page 503 and 504: Model & Method Feasibility Evaluati
Page 505 and 506: Structural Testing for Multithreade
Page 507 and 508: adequate to another criterion. This
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Page 515 and 516: A Tiny Specification Metalanguage W
Page 517 and 518: C. Syntax Extensions The expressive
Page 519 and 520: however, does not allow variables f
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Page 525 and 526: Figure 1. The process for engineeri
Page 527 and 528: Figure 4. The abstractions extracte
Page 529 and 530: way to detect something which could
Page 531 and 532: Algorithm 1: Algorithm to recursive
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Page 535 and 536: A. Pattern Reuse In patterns reuse
Page 537 and 538: IV. SCATTER In order to enhance pat
Page 539 and 540: These latter would be clustered acc
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Page 543 and 544: DC2AP Element and their Application
Page 545 and 546: 21. Consequences Example of use DC2
Page 547 and 548: Legacy2Cloud logic. In this scope,
Page 549: 2.3.1 Complementarity of MDD techno
Page 553 and 554: cess. The proposal follows the ADM
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 ,
Page 561 and 562: Adaptive software should basically
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 577 and 578: the project. This achievement has t
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 593 and 594: Fig. 1: Swing Framework Class Diagr
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Page 599 and 600: 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
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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