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within the umlTUowl prototype because these elements have not been used by CDL-Flex customers and engineers in the past years and also may not be important for the populace of UML modelers outside CDL-Flex projects. Other reasons are that some elements are hard to implement because they can’t be expressed naturally in OWL. For instance, how can someone express a class operation such as , without introducing additional semantics? However, this case is not relevant for the CDL-Flex. The tool should be seen as a recommendation as well. It attempts to suggest, which informations should be provided when publishing an UML transformation tool, based on the experiences collected during the evaluation phase. B. Software Architecture umlTUowl naturally accepts UML diagrams in the specified XMI format. As illustrated in figure 1, at first the XML/XMI files are loaded into memory. Depending on the XMI inputformat a particular converter is selected, which transforms the UML model into a simplified meta model ( ). The meta model basically consists of modules that represent and facilitate all relevant UML model elements in a convenient way. It has been defined analogue to Eclipse MOF, Netbeans MDR or as discussed in [9]. The usage of a meta model has numerous benefits. On the one hand, decoupling of XMI parsing process ( ) and OWL serialization ( ) eases the adoption of additional converters (e.g. Poseidon XMI format or even ER-diagram conversion) and enables automated testing approaches. On the other hand, a meta model can facilitate access to specific meta data elements, which implies that searching, manipulating and referencing of entities is much more comfortable than directly accessing XML. This advantage is also leveraged by the component, which ensures that all classes, attributes and associations are labeled with a unique and OWL 2 DL compatible name, before they are finally serialized into an OWL 2 DL ontology utilizing the OWL API by University of Manchester (OWL API). Decoupling of input (parsing) and output (serialization) process furthermore has the advantage that not only the parser might be replaced, but also the OWL serialization component. Fig. 1: Workflow and software architecture of umlTUowl 1) Harmonizer: The harmonizer component ensures the uniqueness of element names and prepares them for the OWL export. According to the configured strategy all entity names in the meta model are unified, so that each name only occurs once. Furthermore, attribute and association names are converted into common OWL styles. Table II illustrates some of the most considerable harmonizing techniques and how they are applied (the complete example can be found in [4][p.22]).Two strategys have been implemented to handle packages: Depending on the configuration either all meta packages are merged into a single meta model during the harmonizing phase, or all packages remain and are serialized into separated ontologies, subsequently. If all classes are merged into a single ontology, more harmonizing effort is required, since it can happen that semantically inequivalent classes with same class name exist in different packages. These classes have to be prefixed with their package name. Duplicate attributes and association names frequently occur even within a package. They appear, when packages are merged into a single ontology or when the same attribute (or association) name is contained in different UML classes (e.g. age might exist for a class named ”Student“, but also for a class named ”Building“). Therefore, the component offers a set of naming strategys, which can be customized. Thus, attribute names are always renamed (pre- or suffixing class and/or package name), if they occur more than once, although different strategys exist therefore. Associations often exist without label. Even if they are named labels are useless, except the association is navigable. Hence, all associations are renamed, depending on the chosen strategy ( ). For instance, as illustrated in table II, aggregations and compositions can be renamed using supplementary patterns. If only a conventional association is established between two classes, e.g. ”Building“ contains ”Rooms“, this results in two OWL object properties. The first one is named ”buildingHasRoomAssociation“ and the second (inverse) object property is named ”roomHasBuildingAssociation“; if the association is bidirectional navigable, there is no way, to find out, if ”Building“ is contained in ”Room“ or vice versa. 2) Metamodeltoowl: The OWL API by the University of Manchester has been utilized to serialize umlTUowl’s meta model into valid OWL2 DL. Not all UML elements have a matching counterpart. For instance, an abstract UML class will be transformed into an entity with probably specific naming conventions. Depending on the chosen transformation strategy either all UML packages are merged into a single file, or each package is separated into a single OWL ontology. This approach is inspired by previous works, such as [2] and [9]. Attributes are transformed into data properties. XML builtin data types (e.g. ) are supported as well as OWL built-in data types. UML associations result in OWL ob- 12 X: harmonizing carried out using default settings; blank: adapted configuration settings; S: blank + special case 733
Elem. UML Example D 12 Harmonizing result Relevant settings class class class abstract class attribute attribute attribute association composition aggregation association Two classes named Student exist within two different packages. Two classes named Student exist within two different packages. Two classes named Student exist within two different packages. {abstract} Student Two classes, namely Student and Building exist within the same package. Both contain attribute name. Two classes, namely Student and Building exist within the same package. Both contain attribute name. Two classes, namely Student and Building exist within the same package. Both contain attribute name. Unidirectional association named based on between Tree and Trunk. Each Tire belongs to exactly 1 Car at one time. University has Researchers. Three associations: Tank contains Temperature Sensors, Level Sensors and Heaters (class names). All assoc. are labelled with contains. X X X X X S Student Package2 Student Package1 Student Package2 Student Student in out Package1.owl Student in out Package2.owl Abstract Student Building: hasName Student: hasPackage1 StudentName Building: hasName Student: hasStudentName Student: hasStudentName Building: hasBuildingName hasTreeTrunkRelation hasTrunkTreeRelation (inverse). Comment ”Relation originally named ’based on’“ added. hasCarTireComposition isTireOfCar (inverse - part of) hasUniversityResearcher IsResearcherOfUniversity (inverse) All associations are still labelled with contains. One OWL object property named with domain and range will be created. merge-packages=true merge-disable-fixing=true merge-class-prefix={package} merge-packages=true merge-disable-fixing=false merge-class-prefix={package} merge-packages=false abstract-prefix=Abstract abstract-postfix= merge-packages=true data-property-prefix=has merge-attribute-strategy=duplicates merge-attribute-prefix={package-class} merge-packages=true data-property-prefix=has merge-attribute-strategy=duplicates merge-attribute-prefix={class} merge-packages=true data-property-prefix=has merge-attribute-strategy=all merge-attribute-prefix={class} relation-strategy-relation=Relation relation-strategy-1= has{from}{to}{dependingRelation} TABLE II: Examples of implemented harmonizing techniques, depending on settings relation-strategy-1=has{from}{to}{dependRel} relation-strategy-composition-part- 1=is{from}Of{to}; relation-strategy-composition=Composition relation-strategy-1=has{from}{to}{dependRel} relation-strategy-aggregation-part- 1=is{from}Of{to}; relation-strategy-aggregation= allow-multiple-labelled-names=true relation-strategy-1={name} relation-strategy-2=has{from}{to} {dependRel} ject properties, which are restricted by domain and range through their corresponding OWL classes (formally UML classes or interfaces). Equally named associations as well are supported. Thus, domain/range expressions contain concatenated OWL entities, separated by . Example: UML associations and will be transformed into object property with domain and range .Ifan association is navigable in both directions, object properties will be linked as Inverse properties one another. All object properties are added as subclass axioms to their related OWL classes, concerning cardinality. Assuming that a Professor can hold between 0 and 500 lectures, the example above would result in two subclass axioms: and . The mapping mechanism of umlTUowl is detailed in figure 2. All transformed ontologies have been validated manually, by applying ManchesterValidator for OWL2 DL format and by comparing the resulting ontology in Protégé (using the FaCT++ reasoner) [10]). The verification of the ontologies showed that all UML reference models were transformed correctly and completely for all three UML modeling tools and with respect to different configuration settings of umlTUowl. Fig. 2: Mapping of UML elements and OWL entities 734
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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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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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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
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: The tool’s ability to deal with S
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
Page 779 and 780: 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
Page 793 and 794: 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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