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expected reconfiguration. For instance, failures w ere injected to the Camera1 first and then to t he Camera2 to check if the Camera1 and Camera2 failures af fects the Car Detection and Pedestri an components. Also, we tested the reconfigurations for the LRF component that is insignificant to the Car Detection componen, and Pedestrian component that are serious to the Dangerous Situation component. These test results were the same as the expected reconfiguration. Korea [10030826, Development of Reliable OPRoS Framework]. References [Ahn10] Ahn, H., D. Lee, and S. Ahn, “Hiearchical Fa ult Tolerant Architecture For Com ponent-based Service Robots,” 8 th IEEE International Conferenc es on Industrial Informatics(INDIN), Osaka, Japan, 2010, pp.487-492 . Laser Range Finder (LRF) insignificant localization [Jang10] Jang, C., S. Lee, S. Jung, B. Song, R. Kim, S. Kim, and C. Lee, "OPROS: A New Component-Based Robot Software Platform," ETRI Journal, vol.32, no.5, Oct. 2010, pp.646-656. Camera1 Camera2 tolerable Car Detection tolerable tolerable tolerable tolerable Pedestrian serious serious Dangerous Situation serious [Mohamed10a] Mohamed, A., and M. Zulkernine, “Failure type-Aware reliability Assessment Component Failure Dependency,” 4 th International Conference on Secure software Integration and reliability Improvement(SSIRI) Singapore, 2010, pp.98-105. Thermal Imaging Camera (TIC) tolerable Fig. 4 Pedestrian Detection, and Environment Modeling and Perception in UGV Application 4. Conclusions This paper has described a n approach to reconfiguring component-based robot applications using the data dependency relationships between components and their impact analysis. The impact of a c omponent failure to its dependent components are analyzed and classified as different impact levels, such as insignificant, tolerable, serious, and catastrophic. Based on different impact levels, a robot application is reconfigured against a component failure. To validate, a prototype for reconfiguration has been developed with the UGV application and it has been tested with OPROS engine. This paper can have future research. The constraints on impact levels bet ween data dependency relationships between components need be specified formally. The impact level between data dependency relationships can be changed dynamically and should be specified by means of constraints. Also the proposed approach needs to be applied to large-scale robot applications to validate further. Moreover, this paper could be extended to include event and functional dependency relationships between components for reconfiguring a robot application. [Mohamed10b] Mohamed, A., a nd M. Zulkernine, “The Level of De composition Impact on Com ponent Fault Tolerance,” IEEE 34 th Annual Computer Software a nd Applications Conference Workshops(COMPSACW), Seoul, Korea, 2010, pp.57-62. [Popescu10] Popescu, D., “Impact Analysis for Event-Based Components and <strong>Systems</strong>,” ACM/IEEE 32 nd International conference on Software Engineering, Cape Town, South Africa, 2010, pp.401-404. [Roh11] Roh, M. C., J. Byun , and S. Kim, “Design of t he UGV System based on Open Platform Robot of S oftware,” 6 th Korean conference on robots, Seoul, Korea, June, 2011. [Song08] Song, B., S. Jung, C. Jang, and S. Kim, “An introduction to Robot Component Model for OPROS (Opend Platform for Ro botic Services),” International Conference on Simulation, Modeling, and Programming for Autonomous Robots, Venice, Italy, 2008. [Vieira02] Vieira, M., a nd D. Richards on, “Analyzing dependencies in Large Com ponent-based systems,” 17 th IEEE International Conference on A utomated Software Engineering (ASE) , Edinburgh, UK, 2002, pp.241-244. Acknowledgement This work was supported by the Industrial Foundation Technology Development Program of MKE/KEIT, Rep. of 687
Spacemaker: Practical Formal Synthesis of Tradeoff Spaces for Object-Relational Mapping Hamid Bagheri University of Virginia, 151 Engineer’s Way, Charlottesville, VA 22903 USA hb2j@virginia.edu Kevin Sullivan University of Virginia, 151 Engineer’s Way, Charlottesville, VA 22903 USA sullivan@virginia.edu Sang H. Son University of Virginia, 151 Engineer’s Way, Charlottesville, VA 22903 USA son@virginia.edu Abstract—Developing mixed object-relational (OR) mappings that achieve desirable quality attribute tradeoffs for objectoriented applications is difficult, tedious, costly, and error-prone. We contribute a practical, automated technique for exhaustive, formal synthesis of large spaces of such mappings, and the clustering of individual mappings in these spaces into multidimensional quality equivalence classes. This technique can help engineers to design effective persistence layers for object-oriented applications. Our approach is to use a formal language to describe both a space of mappings and multiple quality attribute valuation functions on points in such a space. We use a constraint solver to exhaustively enumerate points in this space and their valuations. We then cluster the results into quality attribute equivalence classes. This work promises to reduce the cost and time required to develop mixed OR mappings, ensure their formal correctness, and help engineers to understand and make tradeoffs quantitatively. We conducted application-oriented experiments to test feasibility and scalability of our approach. In one experiment we synthesized an OR mapping tradeoff space for a real e- commerce application, synthesizing and classifying hundreds of thousands of mappings in just a few minutes. Index Terms—Design; Database; Object-relational mapping; Design space exploration; Alloy Language. I. INTRODUCTION Object-oriented applications often need to use relational databases for persistent storage. Transformations between instance models in these two paradigms encounter the socalled impedance mismatch problem [9]. Object-relational mapping (ORM) systems are now widely used to bridge the gap between object-oriented application models and relational database management systems (DBMS), based on applicationspecific definitions on how object models are to be mapped to database structures. The problem we address is that today one has to choose between automatic generation of mappings using pure mapping strategies [3], [9], or the manual design of mixed mappings, in which different mapping strategies are applied to individual classes rather than to entire inheritance hierarchies. Producing pure mappings automatically is easy, but it often leads to sub-optimal results. Developing mixed mappings by hand can achieve much higher quality, but it is hard and error-prone. Among other things, it requires a thorough understanding of both object and relational paradigms, of large spaces of possible mappings, and of the tradeoffs involved in making choices in these spaces. To address this problem, we present an approach that provides both the quality benefits of mixed mappings and the productivity benefits of automated synthesis. We present a practical formal automated technique, implemented in a prototype tool that we call Spacemaker, for exhaustive synthesis of mixed OR mappings and their classification into quality attribute equivalence classes. We take as inputs a formal object model and optional class-specific mapping strategies for those classes that the user wants mapped in a specific manner. We then use an automated constraint solver to exhaustively generate the space of mappings subject to the given constraints, along with multiple quality attribute measures for each mapping. Next we cluster mappings into quality attribute equivalence classes and present candidates from each class to the engineer along with the measures of its quality attributes. The engineer can then select a mapping to satisfy tradeoff preferences. Our prototype tool uses Alloy as a specification language [10], and the Alloy analyzer as a constraint solver. In more detail, we claim four main contributions: (1) We present what is to our knowledge the first formalization of fine-grained and mixed ORM strategies by means of mapping functions; (2) we contribute a fully automated approach for formally precise synthesis of mapping tradeoff spaces, based on this formalization; (3) we present an experimental demonstration of technical feasibility and scalability to practically meaningful applications; and (4) we develop the Spacemaker tool [1], which we make available to the research and education community. Data from our experiments support the claim that our technique can reduce the time to develop highquality OR mappings, ensure their correctness, and enable engineers to make tradeoffs based on automatically computed quality attributes for a full range of possible mappings rather than on intuition or hard-to-acquire expertise in OR mapping strategies. The rest of this paper is organized as follows. Section II presents our approach. Section III reports and discusses data from the experimental testing of our approach. Section IV surveys the related work. Section V concludes the paper with an outline of our future work. II. APPROACH We present our approach in three parts. We first formalize application of object-relational mapping strategies. We then 688
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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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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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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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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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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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Page 721 and 722: Fig. 3. Mapping diagram for Figure
Page 723 and 724: A formal support for incremental be
Page 725 and 726: machine may be empty which should l
Page 727 and 728: software, based on revised requirem
Page 729 and 730: A Process-Based Approach to Improvi
Page 731 and 732: Appropriate processes m ust support
Page 733 and 734: Figure 2. Reordered layers of the k
Page 735 and 736: Automatic Acquisition of isA Relati
Page 737 and 738: III. VALIDATING THE DIMENSION HEADE
Page 739 and 740: The table title and the dimension s
Page 741 and 742: A light weight alternative for OLAP
Page 743 and 744: i.d. subsets of cubes focused on se
Page 745 and 746: Match production rules Enterprise S
Page 747 and 748: A Tool for Visualization of a Knowl
Page 749 and 750: other ontology visualization tools
Page 751 and 752: shown at Figure 5. Objectives are s
Page 753 and 754: Rendering UML Activity Diagrams as
Page 755 and 756: a = O1 → a → O2 b = O2 → b
Page 757 and 758: ecordProblem → A → reproducePro
Page 759 and 760: umlTUowl - A Both Generic and Vendo
Page 761 and 762: The tool’s ability to deal with S
Page 763 and 764: Elem. UML Example D 12 Harmonizing
Page 765 and 766: 4) Associations: In the first test
Page 767 and 768:
III. GRAPH REPRESENTATION OF CLASS
Page 769 and 770:
as an add-in to popular UML m odeli
Page 771 and 772:
742
Page 773 and 774:
744
Page 775 and 776:
746
Page 777 and 778:
her necessities. However, the progr
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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