SEKE 2012 Proceedings - Knowledge Systems Institute

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There are two possibilities that can be drawn from such scenario: (1) RM is not a commonly performed activity in an SPL project; or, (2) practitioners have not reported their experience about what has been experienced in RM to SPL projects. Such possibilities were confirmed through the studies analyzed during the mapping in SPL and SSD [5]. a) RM activities and steps. A number of common and different activities were identified during the results analysis. In order to understand them, some steps were also considered, as showed in Table III. The use of steps may ease the activities execution. However, for some identified activities and practices, the steps were not mentioned. TABLE III. ACTIVITIES AND STEPS ID Activities and Practices SPL SSD Steps SPL SSD Ac1 Mature Scope Definition Identify the drawbacks in Scope Isolate stable areas from unstable Meetings with the experts Ac2 Risk Identification Apply Interview Apply Brainstorming Analyses Documentation Apply Questionnaire Continuous Identification Early Identification Observation and Expertise Provide Meetings Provide Risk Description Risk Scenario Use of Tool Ac3 Risk Prioritization Checklist Define Level of Exposure Rank the Risks Summarize the Risks Ac4 Risk Reporting ---- Ac5 Team Commitment Define Stakeholders Role Improve the Communication Provide Meetings Stakeholder´s Expertise Training the Team Ac6 Artifacts Reuse Identify reusable assets Define Level of Exposure Define Graphs Define Risk Template Ac7 Provide (SPL) Risks Description Documentation Risk Historic Risk Ranking Risk Scenario Ac8 SPL Management Software Product Line Mapping Artifacts Management Ac9 Define Interview Identify risk through interview Define the Questionnaire Ac10 Mature Domain Definition Preparation, Execution and Analysis Establish a domain analyst role Assess the subdomains Identification of reusable entities Make a domain potential analysis Ac11 SPL Assessment Assess the architecture Identify the strengths and weakness Ac12 Feature Development Features Identification Features Elicitation Feature Model Development Ac13 SPL Variability Variability Assessment Ac14 Requirements Management Requirements Elicitation Ac15 Tool for SPL ---- Continued on next page 643
TABLE IV. CONTINUED FROM PREVIOUS PAGE Ac16 Architecture Definition Architecture Assessment Ac17 SPL Tool ---- Ac18 SPL Testing ---- Ac19 Contingency Plan Identify Contingency Factors Ac20 Cooperative RM ---- Ac21 Define RM Plan Action Plan Ac22 Define Scenario ---- Anticipatory Mitigations Avoid or Prevent the Risk Define Level of Exposure Define Mitigation Plan Mitigation Plan Ac23 Early Identification Risk Transfer Risk Reduction Risk Acceptance Ac24 Provide Communication Provide Meetings Ac25 Provide <strong>Knowledge</strong> ---- Analysis Effects and Causes Analysis Entire Project Analysis Interview Analyse the Documentation Ac26 Define Graphs Risk Analysis Decision Makers Define Level of Exposure Risk Scenario Rank the Risks Stakeholders Influence Apply a Questionnaire Define Level of Exposure Quantify the quality requirements Ac27 Rank the Risks Risk Assessment Risks Categories Training the Team Ac28 Risk Avoidance ---- Effort Factors Managing Changes Organization factors Project factors Risk Classification Ac29 Risk Archetypes Schedule Factors Team factors Technical factors Ac30 RM in Early Stage ---- Ac31 Risk Monitoring Define Stakeholders Roles Early Identification Provide Meetings Review Risk Risk Identification Tracking the Risk Ac32 Risk Pattern Review Risk Continued on next page 644
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SEKE San Francisco Bay July 1-3 201
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Copyright © 2012 by Knowledge Syst
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The 24 th International Conference
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Zhenyu Chen, Nanjing University, Ch
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Riccardo Martoglia, University of M
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Poster/Demo Sessions Co-Chairs Fars
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Evaluating the Cost-Effectiveness o
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Program Understanding Evaluating Op
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An Empirical Study of Execution-Dat
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Computer Forensics: Toward the Cons
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Modeling Bridging KDM and ASTM for
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A Mapping Study on Software Product
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A proposal for the improvement of t
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Panel on Future Trends of Software
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een available for analysis. Social
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The rest of this paper is orga nize
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Now we assume is given, we first pr
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“iphone” and “blackberry” a
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nique estimate the impact set based
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Figure 1. An example to illustrate
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trategy, the results should support
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Figure 1. System Architecture would
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Figure 2. Sequence Diagram of Train
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deployed on Android phone and runs
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the ontology information is only us
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Both kinds of axioms lead to an inh
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engineer and achieving requirements
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elicitation and the act ivities con
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Step 2. Step 3. 1.2. Use of Procedu
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Figure 6.b. Task: Cognitive Analysi
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original User’s Discourse / Speec
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II. RELATED WORK Business processes
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test, we did not find any significa
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For the indicator “requirements a
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the model checkers SPIN [8] and NuS
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allow for the presentation of syste
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the requirement or design phases sh
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using the CR estimates against the
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Data Set TABLE II. DATA SETS Artifa
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Figure 4. Relative Error in the Est
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Take basic requirements from user a
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sent Semantic Web Ontologies. It co
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The rest of the paper is organized
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and the risk level. Existing assess
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diagram could give developers an in
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Figure 2. Co
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• Sensor(Lexical): at the top and
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An Overview of the RSLingo Approach
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Figure 2. Overview of the RSLingo a
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DETECTING EMERGENT BEHAVIOR IN DIST
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Definition 2: For a set of MSCs M,
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Stability of Filter-Based Feature S
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Algorithm 1: Threshold-Based Featur
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MI KS GM AUC PRC S2N RUS35 RUS50 RO
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80
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82
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84
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86
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Cloud Application Resource Mapping
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Fig. 1. UML Collaboration Diagram m
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Fig. 2. UML Activity Diagram modeli
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An Empirical Study of Software Metr
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TABLE I SOFTWARE DATASETS CHARACTER
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1) Classifiers: In this study, soft
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Progressive Clustering with Learned
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IV. DATA SOURCES We have been worki
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meta-data we assign to indicate sig
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Multi-Objective Optimization of Fuz
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uilding FNNs that satisfy different
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VI. EXPERIMENTAL RESULTS The presen
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Generating Performance Test Scripts
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test duration(s) for the scenario(s
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which are composed by the name and
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A Catalog of Patterns for Concept L
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assisted solution for lattice analy
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The complete lattice of the class s
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Client-Side Rendering Mechanism: A
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JavaScript code directly within the
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Fig. 6. CPU usage percentage of pre
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may introduce the extra learning cu
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[9] but no validation is provided i
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One of the st udents from Group B t
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scribed by the equation: where η i
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4 Model Checking DPN We use HyTech
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Recommender systems are usually cla
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such as one week and gradually dete
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•
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146
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Enforcing Contracts for Aspect-orie
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Table 1. Behavioral Rules in LSD [9
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1 public privileged aspect Update {
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Towards More Generic Aspect-Oriente
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vals must be denoted by some notion
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Aspect-Orientation in the Developme
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Table II SELECTED PRIMARY STUDIES #
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Evaluating Open Source Reverse Engi
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Table 1. Candidate reverse engineer
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Some tools may perform the needed f
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Coordination Model to Support Visua
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this CMV approach we employee three
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colored according to the Gradient T
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Improving Program Comprehension in
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The method used to support and eval
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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
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An Approach for Software Component
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properties. In particular, the foll
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Conference Dataset Anatomy Dataset
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equipment maintenance node may cont
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Online Anomaly Detection for Compon
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B. Monitoring Static Method Invocat
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undle which is responsible for pars
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An Exploratory Study of One-Use and
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Figure 1. Mindmap of Reuse Design P
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subject also caused outliers for re
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Choosing licenses in free open sour
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The important aspect is to provide
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that holds a model of each software
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Online Probability Application Char
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TABLE II THE PARAMETERS FOR THE HAR
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[10] N. Gunther, “Hit-and-run tac
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Section 5 d iscusses some related w
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model and the analysis results. The
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tree view of UML model, as show in
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II. BACKGROUND In this section, we
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Algorithm 2 MarkStatements function
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(a) printtok (b) printtok2 (c) sche
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Gupta extended HGS and proposed the
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manage intellectual knowledge with
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D. Manage Training When any trainin
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TABLE II. COMPARISON BETWEEN THE EX
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B. Trace Semantics of DAP Fig. 1. A
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∀ [DR ′ ] []gen [; ] [D]□ [;
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transformation idea in MDA [13] . T
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MappingRule MappingTGtoHP { [Rule I
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executing a continuous transition,
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protocol of DATS we used is properl
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in Boolean search and reasoning has
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to be broken by expelling one of it
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where, among other things: 1. The t
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A never-ending language learner cal
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4. i 2 Learning(DEC, mex) via Bias
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4.4. An Illustrative Example Now le
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Evolutionary Learning and Fuzzy Log
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In addition to the rules, the knowl
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As described in the previous sectio
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the ontology hierarchy; learning a
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three different learning techniques
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TABLE IX. USING SVM FOR LEARNING tr
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Siemens set contains seven C progra
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The program tcas is not included be
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esults. To be more specific, they w
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According to IEEE [10], regression
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(a) Volatility (b) Complexity (c) R
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It mimics the test engineer knowled
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integrate the test results. • Aut
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No tify the cloud controller to get
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of execution-data classification. T
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Figure 1. Boxplots of average AUC r
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anches is more applicable than the
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To validate the proposed approach,
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contains broadcast of Subject to Ob
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S 1a=1; S 2b=5; cobegin { S 3read
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.
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298
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A functionality is the ability of a
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level); in UML/J2EE technology [11]
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• to provide a unified catalogue
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flexibility they provide to model s
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complete in zero-time, and thus hav
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figure assumes the experiment with
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could be a practical way of dealing
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and (iv) the reduced amount of line
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III.BUSINESS RULES FRAMEWORK FOR KN
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explanation for why the program wan
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320
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322
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A model introducing SOAs quality at
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there exist a hierarchical ranking
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Software as a Service: Undo Hernán
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operations consist in reinjection i
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stored. If event failure, external
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ensure users the trustworthiness of
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Patient Doctor Doctor Patient Direc
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going down at a time. This is a rea
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Decidability of Minimal Supports of
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A S 1 1 1 0 0 0 1 1 0 0 0 1 1
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satisfies R( D) R( C) S 1. Compa
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Automated Generation of Concurrent
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target of testing. A blackbox test
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sequence. Each t i θ i in the firi
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SAMAT -AToolforSoftware Architectur
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Figure 4. The SAM Hierarchical Mode
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High-level Petri net Place Place Ty
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verification and thus is often limi
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(r 1 r 2 ...r k ) 1 denotes Path [r
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Fig. 3. Example a 3-compound weakly
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364
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366
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368
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necessary before verification. In t
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CTL formula as: AG (( jp EX( jp ad
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equirements level, like EA-Miner [1
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376
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378
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380
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II. RESOURCE-ORIENTED WORKFLOW MODE
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eventually triggers a co mmon task.
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Input: A resource oriented workflow
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access control exists, the action i
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(ACCDA), is customized to address a
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ICrudSchema and shown in Figure 3.
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Connectors for Secure Software Arch
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Non-repudiation security service pr
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anAsynchronousCustomer InterfaceCon
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How Social Network APIs Have Ended
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OSN Social Feed Unique Features Con
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users can control the reach of thei
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Computer Forensics: Toward the Cons
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In 2009, Cohen et al. in [4] have o
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dcterms:contributor, dcterms:creato
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A Holistic Approach to Software Tra
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Figure 1. Figure 1 shows an overvie
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B. Case study in a proprietary proj
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Pointcut Design with AODL Saqib iqb
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system attributes, methods and exec
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Feature modeling and Verification b
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hasOptionalPart hasPart hasMandato
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A Context Ontology Model for Pervas
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Figure 1. UML view of the model. Th
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Figure 3. Architecture for deliveri
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Ontology-based Representation of Si
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Figure 2. Upper Ontology fragment f
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I2Sim simulation model was transfor
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An ontology-based approach for stor
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fying the equivalences between conc
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• Homonymy conflicts: occur when
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Automatic Generation of Architectur
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we dealt w ith the verticals rules.
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Towards Architectural Evolution thr
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different from replacing it, we dec
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Using FCA-based Change Impact Analy
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Table 2. Impact set of C1,C2,C5 cha
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Figure 3. The PF and PTS results fo
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Forecasting Fault Events in Power D
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which the airport data did not cont
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classified. Recall is the probabili
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Testing Interoperability Security P
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Figure 2. Overview of the Test Gene
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Objective Figure 4. Testing
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A New Approach to Evaluate Path Fea
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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
Page 621 and 622: About the category “Improvements
Page 623 and 624: Identification Guidelines for the D
Page 625 and 626: created according to the preview re
Page 627 and 628: In our analysis, the “Government
Page 629 and 630: In the following, we present the re
Page 631 and 632: USE SCENARIO The application Vuelos
Page 633 and 634: [8] Mayhew, D., “The Usability En
Page 635 and 636: Interaction State Set Structure Dat
Page 637 and 638: B. Component Model Fig. 2 show s th
Page 639 and 640: As a continuation of our research w
Page 641 and 642: PROMETHEE methods [3] belong to a f
Page 643 and 644: comparison rule, a value of 1/9 is
Page 645 and 646: TABLE V: Supermatrix for the exampl
Page 647 and 648: for knowledge sharing. Based on str
Page 649 and 650: y applying hash functions to its su
Page 651 and 652: Empirical Validation of Variability
Page 653 and 654: III. EXPERIMENTAL STUDY In this sec
Page 655 and 656: Table III SPEARMAN’S CORRELATION
Page 657 and 658: A Mapping Study on Software Product
Page 659 and 660: most relevant sources in software e
Page 661 and 662: TABLE IV TOOL’S CLASSIFICATION AC
Page 663 and 664: [2] P. Clements and L. Northrop, So
Page 665 and 666: and future works. II. BACKGROUND ON
Page 667 and 668: System Advanced Standard Module A M
Page 669 and 670: Algorithm 1: Backtracking for MIN-A
Page 671: outcomes from such research fields,
Page 675 and 676: contribution of each study was made
Page 677 and 678: assets, the plugin approach can be
Page 679 and 680: Figure 3: PlugSPL Feature Model Edi
Page 681 and 682: contributions from the several acto
Page 683 and 684: o Guideline 6.4: Softgoal dependenc
Page 685 and 686: descriptions must also b e configur
Page 687 and 688: It means that throughout the develo
Page 689 and 690: B. Instrumentation The experiment w
Page 691 and 692: • External Validity: W e have con
Page 693 and 694: II. THE PROPOSED TAXONOMY The taxon
Page 695 and 696: sub-dimension is categorized as, co
Page 697 and 698: A Proposal of Reference Architectur
Page 699 and 700: Figure. 1. Reference Architecture M
Page 701 and 702: A Variability Management Method for
Page 703 and 704: C. Correctness of configuration fil
Page 705 and 706: means patterns which are shown in s
Page 707 and 708: Tool Support for Anomaly Detection
Page 709 and 710: Figure 1. System overview of the SD
Page 711 and 712: Once the datasets were evaluated us
Page 713 and 714: Reconfiguration of Robot Applicatio
Page 715 and 716: data dependencies required for keep
Page 717 and 718: Spacemaker: Practical Formal Synthe
Page 719 and 720: Fig. 1. The ecommerce object model.
Page 721 and 722: Fig. 3. Mapping diagram for Figure
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A formal support for incremental be
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machine may be empty which should l
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software, based on revised requirem
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A Process-Based Approach to Improvi
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Appropriate processes m ust support
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Figure 2. Reordered layers of the k
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Automatic Acquisition of isA Relati
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III. VALIDATING THE DIMENSION HEADE
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The table title and the dimension s
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A light weight alternative for OLAP
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i.d. subsets of cubes focused on se
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Match production rules Enterprise S
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A Tool for Visualization of a Knowl
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other ontology visualization tools
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shown at Figure 5. Objectives are s
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Rendering UML Activity Diagrams as
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a = O1 → a → O2 b = O2 → b
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ecordProblem → A → reproducePro
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umlTUowl - A Both Generic and Vendo
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The tool’s ability to deal with S
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Elem. UML Example D 12 Harmonizing
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4) Associations: In the first test
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III. GRAPH REPRESENTATION OF CLASS
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as an add-in to popular UML m odeli
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742
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744
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746
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her necessities. However, the progr
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Figure 3. Global Log. of terms. The
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two stages. The first stage is focu
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754
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756
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758
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With the GDUC approach, we can mode
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Figure 6. Three proposals containin
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764
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766
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Proactive Two Way Mobile Advertisem
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information. If more than one adver
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Users can al so publish adv ertisem
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The COIN Platform: Supporting the M
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A-3
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Checking Contracts for AOP using XP
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Maicon B. da Silveira, 112 Aldo Dag
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Seyedehmehrnaz Mireslami, 70 Takao
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Wei Zhang, 422 Wenbo Zhang, 188 Zhi
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Desmond Greer Eric Gregoire Christi
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Poster/Demo Presenter’s Index A A
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SEKE 2012 Proceedings - Knowledge Systems Institute

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