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Towards a Novel Semantic Approach for Process Patterns’ Capitalization and Reuse Nahla JLAIEL RIADI Research Laboratory National School of Computer Science La Manouba, Tunisia Nahla.Jlaiel@riadi.rnu.tn Mohamed BEN AHMED RIADI Research Laboratory National School of Computer Science La Manouba, Tunisia Mohamed.Benahmed@riadi.rnu.tn Abstract—Process patterns are widely used by the software engineering’s community as an excellent mechanism for communicating software development knowledge (experiences and best practices) that has proven to be effective in practice. As a consequence, several description languages and formats have been proposed in the literature dealing with process patterns, in which patterns are described with different terminologies and reused in informal manner. These disparities make capitalization and/or reuse of process patterns, difficult to be achieved. This is why, in this paper, we propose a new semantic approach for process patterns capitalization and reuse, named SCATTER, which aims to disseminate software process best practices by making process patterns described in a unified and formal form. The proposed approach is based on two main processes namely, process patterns warehousing and process patterns mining. Keywords-software process patterns; patterns capitalization; patterns reuse; patterns unification; patterns warehousing; patterns mining; ontologies; information extraction; text mining; ontologies’ population I. INTRODUCTION Nowadays, patterns are increasingly being recognized by software development communities, as an effective method to reuse knowledge and best practices gained during software development processes [1] [2]. In fact, software patterns now exist f or a wide range of topics including process patterns, requirement, analysis, design, implementation or code patterns, test patterns and even maintenance patterns. Concerning process patterns, whose main role is to capitalize good specifications or implementations of a method to be followed to achieve a goal [3], they become commonly used by software development communities as an excellent medium to share software development knowledge that is often encapsulated in experiences and best practices [4] [5] [6]. Indeed, process patterns are growingly being adopted by different development processes such as A gile processes [7], Object-oriented Software Development processes [8], Component Based Software Development processes [9], Service-Oriented Development processes [10] as well as Aspect-oriented Development processes [11]. As co nsequence to the huge proliferation of the process patterns practice, these latter are being used in an informal manner, through traditional textbooks or better with modest hypertext systems providing weak semantic relationships. In addition to the huge number of process patterns that are available in books or Web-based resources [2], they significantly differ in format, coverage, scope, architecture and terminology used [12]. All of these observations conspire to create barriers to the efficient use of process patterns. In fact, patterns users are expected to investigate different patterns resources such as books, magazines, papers and Web collections to find the most appropriate patterns. This investigation really needs cognitive efforts, abilities and time to identify, understand, select, adapt and apply relevant ones. For these reasons, we argue that efforts are needed to more formally capitalize patterns knowledge in order to help software development communities use, reuse and create process patterns during any given software development process. The overall goal of our research is to build up an intelligent framework supporting process patterns capitalization and reuse. Process patterns capitalization should enable patterns’ integration, validation, evolution and/or creation through patterns’ reuse. In other words we aim to cr eate a framework for process patterns’ knowledge management In this paper, we outline the proposed approach SCATTER, acronym for “SemantiC Approach for sofTware process paTErns capitalization and Reuse” which is a new knowledge management approach [13] adapted for software process patterns. The remainder of this paper is organized as follows: section II deals with the context and motivation of our research work. Section III provides background information on the process patterns’ literature review that we carried out and the resulting meta-model forming the building block of the proposed approach. Section IV gives details on how the proposed approach is performed to better disseminate knowledge and best practices within software development communities. Section V concludes the paper by giving a discussion of our contributions as well as an overview of our work in progress. II. CONTEXT AND MOTIVATION This section is devoted to a brief description of our research context in a first subsection and then to the illustration of our research motivation as a second subsection. 505
A. Pattern Reuse In patterns reuse state of the art and practice, there are two complementary processes: a process for the reuse and a process by the reuse [14]. The software engineering process for re use concerns patterns engineering and aims to identify, specify and organize patterns that should be used during the process. The software engineering process by reuse is devoted to patterns engineering allowing the search, selection, adaptation and the integration of patterns for software engineering. As conclusion to the literature review that we carried out on patterns reuse, we noted that a great deal of interest is addressed to product patterns and more precisely design patterns in research as well as in industry. In addition, we derived that most of the deployed efforts to improve pattern reuse are specific to design patterns and could not be adopted or adapted for other pattern types. Among these, we cite the efforts deployed by [15] to automatically detect design patterns’ problem. On the other hand, process patterns are increasingly being described within software development communities but in different forms. In fact, in our literature review, we identified eleven process patterns’ description models that are being adopted separately and independently. In other words, every community creates and manages its own patterns’ collection or system. This would in our view, cripple patterns’ knowledge reuse especially when we find communities using different models to describe their patterns. As a consequence, patterns’ users could only have access to patterns that are maintained by their own community. So, we argue that a holistic approach is required to better reuse and capitalize process patterns’ knowledge. This latter should support the two complementary processes mentioned above, namely: patterns for reuse and patterns by reuse B. Motivating Example To motivate our position, we try to state the problem of model diversity with regard to process patterns description by showing some patterns’ samples. Figure 1 illustrates this by presenting three different process patterns. Pattern A, proposed by Störrle [16], deals with requirements administration using its own vocabulary to describe patterns attributes such as Intent to express the pattern’s problem. Pattern B, described by Dittman [17], handles the technical review process using the term subproblem as a pattern’s problem. Pattern C which is created by Gnatz [18], deals with task analysis and employs another different vocabulary such as Pros and Cons Pros to express pattern’s discussion. As far as we can see, these patterns are not only informally and differently defined but also described in an unstructured manner. We believe that all of these observations conspire to create barriers to patterns share and reuse. This matter of fact leads us to think to unify and to formalize pattern descriptions in order to make them machine- processable so as to more efficiently share and capitalize them. Section III presents the unification meta-model that we created to express process patterns knowledge after the mapping efforts that we made on the different patterns description models. Figure 1. Heterogeneity of process patterns’ descriptions. III. BACKGROUND This section is intended to provide background information for the proposed approach. The first subsection is devoted to sum up results of the study that we carried out in order to assess process patterns representation and reuse within software development communities. The next subsection will exhibit the general structure of the proposed process pattern meta-model as the building block of the proposed approach. A. Process Patterns Literature: Review’s Results Different works have been carried out in the literature of patterns dealing with process patterns’ description and formalization. These are classified into description models such as Ambler [8], RHODES[19], Gnatz [18], P-Sigma [3], Störrle [16] and other as languages, such as PROMENADE [20], PPDL [5], PROPEL [21], PLMLx [22], UML-PP [6] and PPL [23]. Based on eleven evaluation criteria that we fixed in a previous work [24], we assessed the aforementioned works and revealed several lacks, detailed in [12] and [24], creating barriers to patterns’ knowledge capitalization and reuse. Among these, we notice the lacks of architectural as well as terminological consent in patterns descriptions. Pattern A Pattern B Pattern C The lack of architectural consent means that different process pattern descriptions have been proposed using disparate architectures. In fact, when comparing the eleven selected works from the literature, we identified eleven different pattern description facets, namely: 506
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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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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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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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Client-Side Rendering Mechanism: A
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JavaScript code directly within the
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[9] but no validation is provided i
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scribed by the equation: where η i
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such as one week and gradually dete
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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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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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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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complete in zero-time, and thus hav
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figure assumes the experiment with
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III.BUSINESS RULES FRAMEWORK FOR KN
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explanation for why the program wan
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A model introducing SOAs quality at
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Software as a Service: Undo Hernán
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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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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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D. Conclusion Validity Conclusion v
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evaluation of team productivity, qu
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Figure 3: Process Fragment Example
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complexity issues traditionally inv
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Fig. 1: Swing Framework Class Diagr
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ize:Class”, with an empty precond
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Fig. 11: Overwritten Method Fig. 12
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Investigating the use of Bayesian n
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process for the network. Thus, this
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Reuse of Experiences Applied to Req
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Step 1: To start the process, the u
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Specification of Safety Critical Sy
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current belief of agents in order t
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Using the Results from a Systematic
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obtained from the categorization of
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that automatically presents the usa
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Improving a Web Usability Inspectio
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Following the experimental methodol
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About the category “Improvements
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Identification Guidelines for the D
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created according to the preview re
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In our analysis, the “Government
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In the following, we present the re
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USE SCENARIO The application Vuelos
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[8] Mayhew, D., “The Usability En
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Interaction State Set Structure Dat
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B. Component Model Fig. 2 show s th
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As a continuation of our research w
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PROMETHEE methods [3] belong to a f
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comparison rule, a value of 1/9 is
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TABLE V: Supermatrix for the exampl
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for knowledge sharing. Based on str
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y applying hash functions to its su
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Empirical Validation of Variability
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III. EXPERIMENTAL STUDY In this sec
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Table III SPEARMAN’S CORRELATION
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A Mapping Study on Software Product
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most relevant sources in software e
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TABLE IV TOOL’S CLASSIFICATION AC
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[2] P. Clements and L. Northrop, So
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and future works. II. BACKGROUND ON
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System Advanced Standard Module A M
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Algorithm 1: Backtracking for MIN-A
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outcomes from such research fields,
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TABLE IV. CONTINUED FROM PREVIOUS P
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contribution of each study was made
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assets, the plugin approach can be
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Figure 3: PlugSPL Feature Model Edi
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contributions from the several acto
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o Guideline 6.4: Softgoal dependenc
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descriptions must also b e configur
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It means that throughout the develo
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B. Instrumentation The experiment w
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• External Validity: W e have con
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II. THE PROPOSED TAXONOMY The taxon
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sub-dimension is categorized as, co
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A Proposal of Reference Architectur
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Figure. 1. Reference Architecture M
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A Variability Management Method for
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C. Correctness of configuration fil
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means patterns which are shown in s
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Tool Support for Anomaly Detection
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Figure 1. System overview of the SD
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Once the datasets were evaluated us
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Reconfiguration of Robot Applicatio
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data dependencies required for keep
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Spacemaker: Practical Formal Synthe
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Fig. 1. The ecommerce object model.
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Fig. 3. Mapping diagram for Figure
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A formal support for incremental be
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machine may be empty which should l
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software, based on revised requirem
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A Process-Based Approach to Improvi
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Appropriate processes m ust support
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Figure 2. Reordered layers of the k
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Automatic Acquisition of isA Relati
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III. VALIDATING THE DIMENSION HEADE
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The table title and the dimension s
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A light weight alternative for OLAP
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i.d. subsets of cubes focused on se
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Match production rules Enterprise S
Page 747 and 748:
A Tool for Visualization of a Knowl
Page 749 and 750:
other ontology visualization tools
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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
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The tool’s ability to deal with S
Page 763 and 764:
Elem. UML Example D 12 Harmonizing
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4) Associations: In the first test
Page 767 and 768:
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
Page 781 and 782:
two stages. The first stage is focu
Page 783 and 784:
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
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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