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DC2AP: A Dublin Core Application Profile to Analysis Patterns Lucas Francisco da Matta Vegi, Jugurta Lisboa-Filho, Glauber Luis da Silva Costa, Alcione de Paiva Oliveira and José Luís Braga Departamento de Informática Universidade Federal de Viçosa Viçosa-MG, Brazil, 36570-000 lucasvegi@gmail.com, jugurta@ufv.br, glaubercosta@gmail.com, alcione@dpi.ufv.br, zeluisbraga@ufv.br Abstract—Analysis patterns are reusable computational artifacts, aimed at the analysis stage of the process of software development. Although the analysis patterns can facilitate the work of analysts and programmers adding value through reuse of proven useful and tested ideas, the access to them is still very poor because of the way they are usually described and made available. In order to reduce these deficiencies, supporting cataloging and encouraging the reuse of analysis patterns, it was proposed the Analysis Patterns Reuse Infrastructure (APRI). This infrastructure comprises a repository of analysis patterns documented through a specific metadata profile and accessed via web services. Based on the proposal of APRI, this article presents the specific metadata profile to the documentation of analysis patterns called Dublin Core Application Profile to Analysis Patterns (DC2AP). Keywords- Analysis Patterns; Reuse; Metadata Standards; Dublin Core. I. INTRODUCTION Correction of errors made during the encoding of a software is usually more costly than the correction performed during the stages of analysis and design. The costs for correction of errors increase on each stage and in advanced stages they can be up to 100 times higher than in the early stages [1]. Due to the needs of companies, programmers and analysts are constantly pressured to deliver encoded projects of software as soon as possible, and the analysis stage is often left in the background [2]. This common situation in companies ends up generating software errors identified too late, thus burdening the costs of the final product. According to Fernandez and Yuan [2], analysis patterns can make the analysis stage faster and more accurate for developers, thus preventing that this important stage of development is ignored. The analysis patterns are reusable computational artifacts, aimed at the analysis stage of the process of software development. According to Fowler [3], the analysis patterns are ideas proved useful in a given practical context and that may be useful in other contexts. Although the analysis patterns can facilitate the work of analysts and programmers adding value through reuse of proven useful and tested ideas, the access to them is still very poor [4]. So far there is n o template to specify the analysis patterns that is widely accepted making each s et of analysis patterns is specified according to the preferences of its authors. In addition to not having a pattern specification, the analysis patterns are normally provided in scientific books and papers which are restricted access means and do not allow the efficient retrieval of patterns performed, for example, through a search tool [4]. In order to minimize these problems of specification and retrieval of analysis patterns, it w as proposed the Analysis Patterns Reuse Infrastructure (APRI) [5]. This infrastructure, which was inspired by the Spatial Data Infrastructure (SDI) [6], consists of a repository of analysis patterns, documented in a specific metadata profile and accessed via web services. This article proposes a specific metadata profile to the documentation of analysis patterns compatible with the APRI [5]. The metadata profile is based on the Dublin Core metadata standard [7] and on the template proposed in [8] and [9] to specify analysis patterns. The remainder of this paper is organized as follows: Section 2 describes related works to documentation of analysis patterns and to the Dublin Core metadata standard. Section 3 describes the proposed metadata profile. Section 4 presents an example of analysis pattern specified with the proposed metadata profile and Section 5 presents some conclusions and possible future works. II. RELATED WORK A. Documentation and Organization of Analysis Patterns Documentation of analysis patterns is an important way for contextualizing the reuse scope of a pattern and for enabling the sharing of knowledge among designers. However, this documentation is performed in a heterogeneous manner among the authors, since there is no standardized way to specify analysis patterns [4]. There are many approaches to specify analysis patterns, ranging from non-formalized textual descriptions to formalized descriptions based on templates. Some analysis patterns specified in a n on-formalized textual manner can be found in [3] and [10]. This little formal way of describing a analy sis pattern affects reuse, because it makes harder for designers to quickly understand the contextual scope of patterns, and, mostly, it limits the retrieval of analysis patterns through computerized search engines. Thus important detailed information for designers may not be 511
described or even r etrieved, thus limiting the spread of these patterns and thereby their potential for reuse. Analysis patterns have also been described through the use of templates, which are st ructures with predetermined topics similar to those used to describe design patterns [11]. Usually a template is composed of essential topics such as context, problem, motivation and solution [3], combined with other specific topics defined by their authors. Some analysis patterns documented through templates can be found in [12] and [13]. Meszaros and Doble [14] present in their work a template composed of topics: name, problem, context, motivation, solution, participants and related patterns. This template was used in [12]. Pantoquilho, Raminhos and Araujo [8] and Raminhos et al. [9] present in their work a proposal of detailed template specifically for documentation of analysis patterns. This template combines common topics used previously by several authors, with new topics aimed at describing the analysis patterns more broadly. Besides adequate documentation, another important factor to increase the potential for reuse of analysis patterns is th e way they are organized and therefore available because before a pattern is applied to a project, the designer needs to know of its existence and then select it [4]. Usually analysis patterns are organized together forming collections of patterns, and in one collection, usually the patterns are documented homogeneously by the same author, although this is not a rule. The collection of analysis patterns can have different formats, such as books, articles and websites, and they can still be classified as pattern languages and pattern catalogues [4]. The pattern languages are basically collections of analysis patterns aimed at solving a specific problem. In a pattern language, the patterns are related to each other and must follow application rules, for example, the order in which they must be applied to solve the problem in question [4]. The pattern catalogues are collections of analysis patterns not necessarily related, but organized based on criteria in common and searchable. Fowler's book [3] is an example of an analy sis pattern catalog, because the organization of the patterns described in that book was obtained from groups of patterns with application domains in common and they may be found by potential users through a table of contents [4]. B. Dublin Core Metadata Initiative Metadata are data about data, i.e., information that makes it possible to know the resources of the data. They can be used to standardize data representation based on the description of their authors, quality levels, application domains and other elements, thereby encouraging the appropriate reuse of data [6]. The metadata standards are metadata structures used to describe the data. According to the domain of the data to be described, these description structures may have variations compared to other domains due to peculiarities of each of them. Thus, several metadata standards have already been proposed in order to meet the specific needs of some domains. Examples of metadata standards for specific domains can be found in [15] and [16]. The Dublin Core metadata standard [7] appeared in 1995 from a workshop held in Dublin city in the U.S. state of Ohio. This event brought together professionals from several fields of knowledge in order to establish a generic metadata standard composed of a small set of recurring elements in all areas [17]. This metadata standard has two levels, Simple Dublin Core and Qualified Dublin Core. The Simple Dublin Core consists of fifteen elements, as the Qualified Dublin Core has seven additional elements that allow more detailed descriptions of data. Beyond the twenty-two elements that compose the levels of the Dublin Core standard, it has many element refinements that can be used to specialize the semantics of an element in certain situations and thus facilitate the discovery of the data [18]. A major advantage of the Dublin Core standard is its versatility. Although it is ve ry simple and does no t provide enough resources to describe data of complex domains [17], it can be specialized from the creation of application profiles for specific domains [6]. Coyle and Baker [19] describe in their work the basic steps for creating a Dublin Core application profile. An example of a Dublin Core application profile for specific domain can be found in [20]. III. A DUBLIN CORE APPLICATION PROFILE TO ANALYSIS PATTERNS The Dublin Core Application Profile to Analysis Patterns (DC2AP) was developed based on the template proposed in [8] to specify analysis patterns. The main objectives of DC2AP are to improve retrieval and reuse of analysis patterns by means of a description that allows a more precise treatment performed by a computer, providing detailed information about the analysis patterns that were not retrieved by search engines. A. Mapping between Dublin Core Metadata Elements and Pantoquilho’s Analysis Pattern Template In contrast to the Dublin Core metadata standard, which is generic and therefore aimed to document resources of several domains, the template proposed in [8] by Pantoquilho et al. is designed specifically for the documentation of analysis patterns, so it is rich in specific details of this domain. Due to such level of detail, this template was chosen to be used as a basis for the creation of DC2AP. An important task for the creation of DC2AP was the realization of a mapping between the elements proposed by the Dublin Core and elements of the template proposed in [8]. In this mapping process, elements of both structures were compared and classified based on their semantic correspondences and some conceptual conflicts were identified. These conflicts are characterized by similar concepts that are expressed differently by each of the mapped structures. After identifying and r esolving the existing conceptual conflicts between structures it became possible to combine the elements of Dublin Core and Pantoquilho’s template, thus creating a single structure free of redundancies and semantic inconsistencies. Table 1 shows the result of the mapping between the Dublin Core standard and Pantoquilho’s template. Although Table 1 sho ws only the mapping between the 512
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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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Evaluating the Cost-Effectiveness o
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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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DETECTING EMERGENT BEHAVIOR IN DIST
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Multi-Objective Optimization of Fuz
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VI. EXPERIMENTAL RESULTS The presen
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Generating Performance Test Scripts
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A Catalog of Patterns for Concept L
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Enforcing Contracts for Aspect-orie
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Aspect-Orientation in the Developme
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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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An Exploratory Study of One-Use and
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subject also caused outliers for re
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Choosing licenses in free open sour
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Online Probability Application Char
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TABLE II THE PARAMETERS FOR THE HAR
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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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Gupta extended HGS and proposed the
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4.4. An Illustrative Example Now le
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Evolutionary Learning and Fuzzy Log
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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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esults. To be more specific, they w
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integrate the test results. • Aut
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Patient Doctor Doctor Patient Direc
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satisfies R( D) R( C) S 1. Compa
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SAMAT -AToolforSoftware Architectur
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Figure 4. The SAM Hierarchical Mode
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Input: A resource oriented workflow
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access control exists, the action i
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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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Computer Forensics: Toward the Cons
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A Holistic Approach to Software Tra
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system attributes, methods and exec
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Feature modeling and Verification b
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different from replacing it, we dec
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Using FCA-based Change Impact Analy
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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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Page 583 and 584: C. Dependent Variables and Measures
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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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[2] P. Clements and L. Northrop, So
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and future works. II. BACKGROUND ON
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System Advanced Standard Module A M
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Algorithm 1: Backtracking for MIN-A
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outcomes from such research fields,
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TABLE IV. CONTINUED FROM PREVIOUS P
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contribution of each study was made
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assets, the plugin approach can be
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Figure 3: PlugSPL Feature Model Edi
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contributions from the several acto
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o Guideline 6.4: Softgoal dependenc
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descriptions must also b e configur
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It means that throughout the develo
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B. Instrumentation The experiment w
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• External Validity: W e have con
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II. THE PROPOSED TAXONOMY The taxon
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sub-dimension is categorized as, co
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A Proposal of Reference Architectur
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Figure. 1. Reference Architecture M
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A Variability Management Method for
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C. Correctness of configuration fil
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means patterns which are shown in s
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Tool Support for Anomaly Detection
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Figure 1. System overview of the SD
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Once the datasets were evaluated us
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Reconfiguration of Robot Applicatio
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data dependencies required for keep
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Spacemaker: Practical Formal Synthe
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Fig. 1. The ecommerce object model.
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Fig. 3. Mapping diagram for Figure
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A formal support for incremental be
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machine may be empty which should l
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software, based on revised requirem
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A Process-Based Approach to Improvi
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Appropriate processes m ust support
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Figure 2. Reordered layers of the k
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Automatic Acquisition of isA Relati
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III. VALIDATING THE DIMENSION HEADE
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The table title and the dimension s
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A light weight alternative for OLAP
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i.d. subsets of cubes focused on se
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Match production rules Enterprise S
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A Tool for Visualization of a Knowl
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other ontology visualization tools
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shown at Figure 5. Objectives are s
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Rendering UML Activity Diagrams as
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a = O1 → a → O2 b = O2 → b
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ecordProblem → A → reproducePro
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umlTUowl - A Both Generic and Vendo
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The tool’s ability to deal with S
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Elem. UML Example D 12 Harmonizing
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4) Associations: In the first test
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III. GRAPH REPRESENTATION OF CLASS
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as an add-in to popular UML m odeli
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742
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744
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746
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her necessities. However, the progr
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Figure 3. Global Log. of terms. The
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two stages. The first stage is focu
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754
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756
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758
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With the GDUC approach, we can mode
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Figure 6. Three proposals containin
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764
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766
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Proactive Two Way Mobile Advertisem
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information. If more than one adver
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Users can al so publish adv ertisem
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The COIN Platform: Supporting the M
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A-3
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Checking Contracts for AOP using XP
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Maicon B. da Silveira, 112 Aldo Dag
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Seyedehmehrnaz Mireslami, 70 Takao
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Wei Zhang, 422 Wenbo Zhang, 188 Zhi
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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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