708 research outputs found

    Transforming Graph Representations for Statistical Relational Learning

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    Relational data representations have become an increasingly important topic due to the recent proliferation of network datasets (e.g., social, biological, information networks) and a corresponding increase in the application of statistical relational learning (SRL) algorithms to these domains. In this article, we examine a range of representation issues for graph-based relational data. Since the choice of relational data representation for the nodes, links, and features can dramatically affect the capabilities of SRL algorithms, we survey approaches and opportunities for relational representation transformation designed to improve the performance of these algorithms. This leads us to introduce an intuitive taxonomy for data representation transformations in relational domains that incorporates link transformation and node transformation as symmetric representation tasks. In particular, the transformation tasks for both nodes and links include (i) predicting their existence, (ii) predicting their label or type, (iii) estimating their weight or importance, and (iv) systematically constructing their relevant features. We motivate our taxonomy through detailed examples and use it to survey and compare competing approaches for each of these tasks. We also discuss general conditions for transforming links, nodes, and features. Finally, we highlight challenges that remain to be addressed

    Automatically assessing and improving code readability and understandability

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    Supporting Reengineering Scenarios with FETCH: an Experience Report

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    The exploration and analysis of large software systems is a labor-intensive activity in need of tool support. In recent years, a number of tools have been developed that provide key functionality for standard reverse engineering scenarios, such as (i) metric analysis; (ii) anti-pattern detection; (iii) dependency analysis; and (iv) visualization. However, either these tools support merely a subset of this list of scenarios, they are not made available to the research community for comparison or extension, or they impose strict restrictions on the source code. Accordingly, we observe a need for an extensible and robust open source alternative, which we present in this paper. Our main contributions are (i) a clarification of useful reverse engineering scenarios; (ii) a comparison among existing solutions; and (iii) an experience report on four recent cases illustrating the usefulness of tool support for these scenarios in an industrial setting

    Configurable Software Performance Completions through Higher-Order Model Transformations

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    Chillies is a novel approach for variable model transformations closing the gap between abstract architecture models, used for performance prediction, and required low-level details. We enable variability of transformations using chain of generators based on the Higher-Order Transformation (HOT). HOTs target different goals, such as template instantiation or transformation composition. In addition, we discuss state-dependent behavior in prediction models and quality of model transformations

    Seamlessness as a desirable aspect of quality for MDE: the contribution of object-relational database structure

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    Architectural Design Decision Documentation through Reuse of Design Patterns

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    While design decisions on the application of architectural design patterns involve complex trade-offs between functionality and quality properties, such decisions are often spontaneous, and documentation of decisions and trace links to related artefacts is usually insufficient. The approach proposed in this thesis provides a support to overcome these problems. It combines support for evaluation of design pattern application, and semi-automated documentation of decisions and trace links

    Software quality attribute measurement and analysis based on class diagram metrics

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    Software quality measurement lies at the heart of the quality engineering process. Quality measurement for object-oriented artifacts has become the key for ensuring high quality software. Both researchers and practitioners are interested in measuring software product quality for improvement. It has recently become more important to consider the quality of products at the early phases, especially at the design level to ensure that the coding and testing would be conducted more quickly and accurately. The research work on measuring quality at the design level progressed in a number of steps. The first step was to discover the correct set of metrics to measure design elements at the design level. Chidamber and Kemerer (C&K) formulated the first suite of OO metrics. Other researchers extended on this suite and provided additional metrics. The next step was to collect these metrics by using software tools. A number of tools were developed to measure the different suites of metrics; some represent their measurements in the form of ordinary numbers, others represent them in 3D visual form. In recent years, researchers developed software quality models which went a bit further by computing quality attributes from collected design metrics. In this research we extended on the software quality modelers’ work by adding a quality attribute prioritization scheme and a design metric analysis layer. Our work is all focused on the class diagram, the most fundamental constituent in any object oriented design. Using earlier researchers’ work, we extract a class diagram’s metrics and compute its quality attributes. We then analyze the results and inform the user. We present our figures and observations in the form of an analysis report. Our target user could be a project manager or a software quality engineer or a developer who needs to improve the class diagram’s quality. We closely examine the design metrics that affect quality attributes. We pinpoint the weaknesses in the class diagram, based on these metrics, inform the user about the problems that emerged from these classes, and advice him/her as to how he/she can go about improving the overall design quality. We consider the six basic quality attributes: “Reusability”, “Functionality”, “Understandability”, “Flexibility”, “Extendibility”, and “Effectiveness” of the whole class diagram. We allow the user to set priorities on these quality attributes in a sequential manner based on his/her requirements. Using a geometric series, we calculate a weighted average value for the arranged list of quality attributes. This weighted average value indicates the overall quality of the product, the class diagram. Our experimental work gave us much insight into the meanings and dependencies between design metrics and quality attributes. This helped us refine our analysis technique and give more concrete observations to the user

    Management of data quality when integrating data with known provenance

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