Refactorings of Design Defects Using Relational Concept Analysis

<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Fuzzy relational formal concept analysis (FRCA)</i> mines collections of fuzzy concept lattices from <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">fuzzy relational context families</i> , which are special datasets made of fuzzy formal contexts and fuzzy relations between objects of different types. Mainly, FRCA consists of the following procedures: first, an initial fuzzy relational context family is transformed into a collection of fuzzy formal contexts; second, a fuzzy concept lattice is generated from each fuzzy formal context by using one of the techniques existing in the literature. The principal tools to transform a fuzzy context family into a set of fuzzy formal contexts are the so-called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">fuzzy scaling quantifiers</i> , which are particular fuzzy quantifiers based on the concept of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">evaluative linguistic expression</i> . FRCA can be applied whenever information needs to be extracted from multirelational datasets including vagueness, and it can be viewed as an extension of both <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">relational concept analysis</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">fuzzy formal concept analysis</i> . This article contributes to the development of fuzzy relational concept analysis by achieving the following goals. First of all, we present and study a new class of fuzzy quantifiers, called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">t-scaling quantifiers</i> , to extract fuzzy concepts from fuzzy relational context families. Subsequently, we provide an algorithm to generate, given a t-scaling quantifier, a collection of fuzzy concept lattices from a special fuzzy relational context family, which is composed of a pair of fuzzy formal contexts and a fuzzy relation between their objects. After that, we introduce an ordered relation on the set of all t-scaling quantifiers, which allows us to discover a correspondence among fuzzy concept lattices deriving from different t-scaling quantifiers. Finally, we discuss how the results obtained for t-scaling quantifiers can be extended to the class of fuzzy scaling quantifies. Therefore, this analysis highlights the main differences between t-scaling and fuzzy quantifiers.

Design defects come from poor design choices and have the effect of degrading the quality of object-oriented designs.Therefore, they present opportunities for improvements.However, design defects have not been precisely specified and there are few appropriate tools that allow their detection as well as their correction. Our goal is to provide a systematic method to specify systematically design defects precisely and to generate automatically detection and correction algorithms from their specifications. The detection algorithms are based not only on metrics but also on semantical and structural properties whereas the correction algorithms are based on refactorings. We apply and validate these algorithms on open-source object-oriented programs to show that our method allows a systematic specification, a precise detection, and a suitable correction of design defects.

Relational Concept Analysis (RCA) is an extensionDolques, Xavier Le Ber, Florence Huchard, Marianne Nebut, Clémentine to the Formal Concept Analysis (FCA) which is an unsupervised classification method producing concept lattices. In addition RCA considers relations between objects from different contexts and builds a set of connected lattices. This feature makes it more intuitive to extract knowledge from relational data and gives richer results. However, data with many relations imply scalability problems and numerous results that are difficult to exploit. We propose in this article a possible adaptation of RCA to explore relations in a guided way in order to increase the performance and the pertinence of the results. We also present an application of exploratory RCA to environmental data for extracting knowledge on water quality of watercourses.

We propose an automated method and tool for learning ontologies from data. We rely on Relational Concept Analysis (an extension of Formal Concept Analysis) to learn concepts with their instances, relations between these concepts, and properties of these relations, such as symmetry, transitivity and reflexivity. The learned model is formalised in Description Logics. In addition, since we have access to all learned axioms, the learned model is a white box one.

To gain an in-depth understanding of human diseases, biologists typically mine patient data for relevant patterns. Clinical datasets are often unlabeled and involve features, a.k.a. markers, split into classes w.r.t. biological functions, whereby target patterns might well mix both levels. As such heterogeneous patterns are beyond the reach of current analytical tools, dedicated miners, e.g. for association rules, need to be devised. Contemporary multi-relational (MR) association miners, while capable of mixing object types, are rather limited in rule shape (atomic conclusions) while ignoring feature composition. Our own approach builds upon a MR-extension of concept analysis further enhanced with flexible propositionnalisation operators and dedicated MR modeling of patient data. The resulting MR association miner was validated on a pediatric oncology dataset.

Recently, cloud computing has been combined with big data processing leading to a new model of services called big services. This model addresses the customers' complex requirements by reusing and aggregating existing services from various domains and delivery models, and from multiple cloud availability zones. Existing web/cloud service composition approaches are not adequate for the big service context due to many reasons, including the large volume of data, the cross-domain and cross-cloud interoperability issues, etc. Considering the aforementioned facts, we provide a solution to the big service composition issue, by taking advantage of relational concept analysis (RCA), as a clustering method, and composite particle swarm optimisation (CPSO), as an optimisation technique. RCA is used to model the big service environment, whereas CPSO helps continuously optimising the quality of big service composition. The implementation and experimental studies on our approach have proven its feasibility and efficiency.

Design defects are one of the main reasons for the decline of software design quality. Effective detection of design defects plays an important role in improving software maintainability and scalability. On the basis of defining software design defects, according to C&K design metrics and heuristics, this paper extracts the relevant features of design defects. Based on classical machine learning methods, classifiers are trained for design defect, and candidate designs are classified by classifiers, so as to identify whether there is a design defect in the design. Experiments show that the method has high accuracy and recall rate in identifying design defects.

The system catalog of a database with explicit schemas contains among other things information about the structure of the database. Queries based on the system catalog allow us to search occurrences of database design antipatterns (database design flaws). In this paper, we present the results of an evaluation of a set of SQL databases. We used the queries that were presented in the previous paper on this topic. A goal of the research is to further experimentally evaluate the queries. We present findings about the queries as well as evaluated databases. In addition, we propose more questions about the design of conceptual schemas of SQL databases that can be answered by querying their system catalogs. The use of the queries would allow us to partially automate the process of evaluating structure and constraints of existing databases and detecting design flaws.

В статье рассматривается эффективность проектных решений BIM. В работе представлены результаты исследований в области использования инновационных технологий при проектировании зданий. Рассмотрены современные технологии информационного моделирования, в частности BIM, которые позволяют представить здание не только в трехмерном пространстве, но и позволяют проектировать его в динамической системе «проектирование – строительство – эксплуатация». Опыт использования BIM-технологий основан на выявлении ошибок на начальном этапе проектирования и возможности их устранения на этом же этапе. Анализ механизмов обнаружения проектных ошибок и дефектов лег в основу поставленной в работе задачи. Проанализирована модель об обнаружении дефектов. The paper discusses the effectiveness of BIM design solutions. The paper presents the results of research in the field of using innovative technologies in building design. Modern information modeling technologies, in particular BIM, which allow to represent a building not only in three-dimensional space, but also allow to design it in a dynamic system "design - construction - operation" are considered. The experience of using BIM-technologies is based on the detection of errors at the initial stage of design and the possibility of their elimination at the same stage. The analysis of mechanisms of detection of design errors and defects formed the basis of the task set in the paper. The model about defect detection is analyzed.

Improving the design quality of large object‐oriented systems during maintenance and evolution is widely regarded as a high‐priority objective. Furthermore, for such systems that are subject to frequent modifications, detection and correction of design defects may easily become a very complex task that is even not tractable for manual handling. Therefore, the use of automatic or semi‐automatic detection and correction techniques and tools can assist reengineering activities. This paper proposes a framework whereby object‐oriented metrics can be used as indicators for automatically detecting situations for particular transformations to be applied in order to improve specific design quality characteristics. The process is based both on modeling the dependencies between design qualities and source code features, and on analyzing the impact that various transformations have on software metrics that quantify the design qualities being improved. Copyright © 2004 John Wiley &amp; Sons, Ltd.

&lt;div class="htmlview paragraph"&gt;Thermal testing of spacecraft electronic units prior to flight provides effective detection of design, process and workmanship defects. Thermal testing subjects units to cold and hot thermal environments beyond those expected in flight. The strength of screening effectiveness depends upon the number of cycles, the temperature range, and the temperature transition rate. MIL-HDBK-344 provides insight into the incurred stresses and quantitative value (precipitation efficiency) of the screening environment using these three test parameters.&lt;/div&gt; &lt;div class="htmlview paragraph"&gt;In this paper, MIL-HDBK-344 topics applicable to thermal testing of space hardware are summarized and comparisons are made between test environment strengths computed from MIL-HDBK-344 and MIL-STD-1540E. The weighting of these aforementioned test parameters in the precipitation efficiency equation are discussed and assessed. It was found that the fatigue equivalency equations in MIL-STD-1540E specify a significantly larger number of required cycles for various test temperature ranges as compared to cycles computed from MIL-HDBK-344 equations.&lt;/div&gt;

In this paper, we introduce a community detection approach from heterogeneous multi-relational network which incorporate the multiple types of objects and relationships, derived from a bibliographic networks. The proposed approach performs firstly by constructing the relation context family (RCF) to represent the different objects and relations in the multi-relational bibliographic networks using the Relational Concept Analysis (RCA) methods; and secondly by exploring such RCF for community detection. Experiments performed on a dataset of academic publications from the Computer Science domain enhance the effectiveness of our proposal and open promising issues.

A design flaw is indicative of a potential shortcoming in the construction of a software system and can result in a reduction in the quality of the software. This study offers a means of detecting design flaws through the use of ontology-assisted flaw description and declarative meta programming. Ontology flaw structures are used to describe the flaw domains, while the use of a declarative-based method allows the design flaws which arise within an object-oriented system to be altered into these structures, thereby permitting their detection at the metalevel when declarative meta programming is employed. This research uses the method described in order to detect a number of design flaws which are already well-documented. The findings demonstrate that the method is successful in detecting those flaws, and that structural design flaw detection is particularly effective.

статья посвящена вопросам обнаружения и визуализации дефектов проектирования. Известно, что архитектура программного обеспечения имеет решающее значение при его разработке. В ходе разработки и последующего развития программного обеспечения сложность программы накапливается в результате различных действий разработчиков, например, в результате добавления новых функций и, возможно, внесения ошибок. Данные действия часто приводят к деградации архитектуры, что существенно осложняет дальнейшее развитие программных систем и их сопровождения. Большая часть усилий по сопровождению программных систем вызвана дефектами проектирования архитектуры системы. Группы разработчиков могут чувствовать все большую сложность в сопровождении системы по мере деградации архитектуры. Однако, зачастую им может быть трудно понять причину такой сложности и точно определить какие дефекты архитектуры вызывают их трудности в сопровождении и развитии проекта. Безусловно, существуют многочисленные метрики для оценки работоспособности программного обеспечения, однако не все из них способны обеспечить надежность, необходимую для сравнения работоспособности различных систем или вариантов развития системы. В рамках данной статьи проведен обзор и анализ результатов оценки и апробации инструментов обнаружения дефектов проектирования на примере JUnit и MobileMedia. Рассмотрены ключевые функции этих инструментов на примере inFusion, JDeodorant, PMD, JSpIRIT. the article is devoted to the issues of detection and visualization of design defects. Software architecture is known to be crucial in its development. During software development and subsequent development, program complexity accumulates as a result of various developer actions, such as adding new features and possibly making errors. These actions often lead to degradation of the architecture, which significantly complicates the further development of software systems and their maintenance. Much of the effort to maintain software systems is caused by system architecture design defects. Development teams can feel increasing complexity accompanied by the system as the architecture degrades. However, they may often find it difficult to understand the cause of this complexity and to determine exactly what architecture defects cause them to be difficult to accompany and develop the project. Of course, there are numerous metrics for evaluating software performance, but not all are capable of providing the reliability needed to compare the performance of different systems or system development options. This article reviews and analyses the results of evaluation and testing of design defect detection tools using JUnit and MobileMedia. The key functions of these tools are discussed on the example inFusion, JDeodorant, PMD, JSpIRIT.

In the present research, we examined the solution of a “Hypothesis Testing for Paired Samples t-test” problem that can be ideally subdivided into four parts: (1) hypothesis generation and formal expression, (2) calculation of the t value, (3) identification of the correct critical t value for the acceptance-refusal of H0, and (4) the assumption of the correct decision from the comparison between the calculated t and the critical t value. Different response patterns for the problem were obtained. The derived structure represents a partial order. The relation between response patterns and the single elements of the main exercise can be well represented within the framework of the Formal Concept Analysis. From the analysis of the response patterns of a sample of 114 students in a psychometrics course, we derived a Formal Context where the six parts of the “Hypothesis Testing for Paired Samples t-test” exercise were the formal attributes and the response patterns were the formal objects. In our sample only 25 out of the 26 possible response patterns were encountered. A Logistic Model has been applied to develop a measure of the adequacy of each solution’s pattern actually observed. The obtained results are discussed.