Unified Modeling Language Class Diagrams Research Articles

Proposal of a similarity measure for unified modeling language class diagram images using convolutional neural network

The unified modeling language (UML) represents an essential tool for modeling and visualizing software systems. UML diagrams provide a graphical representation of a system's components. Comparing and processing these diagrams, for instance, can be complicated, especially as software projects grow in size and complexity. In such contexts, deep learning techniques have emerged as a promising solution for solving complex problems. One of these crucial problems is the measurement of similarity between images, making it possible to compare and calculate the differences between two given diagrams. The present work intends to build a method for calculating the degree of similarity between two UML class diagrams. With a goal to provide teachers a helpful tool for assessing students' UML class diagrams.

Supervised Classification of UML Class Diagrams Based on F-KNB

Often most software development doesn’t start from scratch but applies previously developed artifacts. These reusable artifacts are involved in various phases of the software life cycle, ranging from requirements to maintenance. Software design as the high level of software development process has an important impact on the following stages, so its reuse is gaining more and more attention. Unified modeling language (UML) class diagram as a modeling tool has become a de facto standard of software design, and thus its reuse also becomes a concern accordingly. So far, the related research on the reuse of UML class diagrams has focused on matching and retrieval. As a large number of class diagrams enter the repository for reuse, classification has become an essential task. The classification is divided into unsupervised classification (also known as clustering) and supervised classification. In our previous work, we discussed the clustering of UML class diagrams. In this paper, we focus on only the supervised classification of UML class diagrams and propose a supervised classification method. A novel ensemble classifier F-KNB combining both dependent and independent construction ideas is built. The similarity of class diagrams is described, in which the semantic, structural and hybrid matching is defined, respectively. The extracted feature elements are used in base classifiers F-KNN and F-NBs that are constructed based on improved K-nearest neighbors (KNNs) and Naive Bayes (NB), respectively. A series of experimental results show that the proposed ensemble classifier F-KNB shows a good classification quality and efficiency under the condition of variable size and distribution of training samples.

Model-driven architecture: generating models from Symfony framework

The web application development industry is constantly growing due to the extensive use of web applications in different devices, most of them run on Android, iOS, and Windows Phone operating systems. However, the development of applications designed for platforms requires more concerns such as code efficiency, interaction with devices, and speed of market penetration. The model-driven approach (MDA) combined with unified modeling language (UML) could provide abstraction and automation for software developers. This paper presents an MDA approach for the development of web applications based on the Symfony framework, UML modeling, model transformation, and then automatic code generation in order to facilitate and accelerate the development of web applications. The first step of this work is to establish the metamodel of Symfony framework and the metamodel of UML class diagram. In the second step, the various transformation rules between the source and target metamodels are first defined. Atlas transformation language (ATL) implements these rules. The result of this transformation is a platform-specific model (PSM) represented by Ecore language. The generated PSM model represents the input model of model-to-code (M2C) transformation for generating the code of web applications. To validate this work, we have implemented a case study.

Policy components – a conceptual model for modularizing and tailoring of information security policies

PurposeThis paper aims to propose a conceptual model of policy components for software that supports modularizing and tailoring of information security policies (ISPs).Design/methodology/approachThis study used a design science research approach, drawing on design knowledge from the field of situational method engineering. The conceptual model was developed as a unified modeling language class diagram using existing ISPs from public agencies in Sweden.FindingsThis study’s demonstration as proof of concept indicates that the conceptual model can be used to create free-standing modules that provide guidance about information security in relation to a specific work task and that these modules can be used across multiple tailored ISPs. Thus, the model can be considered as a step toward developing software to tailor ISPs.Research limitations/implicationsThe proposed conceptual model bears several short- and long-term implications for research. In the short term, the model can act as a foundation for developing software to design tailored ISPs. In the long term, having software that enables tailorable ISPs will allow researchers to do new types of studies, such as evaluating the software's effectiveness in the ISP development process.Practical implicationsPractitioners can use the model to develop software that assist information security managers in designing tailored ISPs. Such a tool can offer the opportunity for information security managers to design more purposeful ISPs.Originality/valueThe proposed model offers a detailed and well-elaborated starting point for developing software that supports modularizing and tailoring of ISPs.

Design and evaluation of a collaborative UML modeling environment in virtual reality.

Modeling is a key activity in conceptual design and system design. Through collaborative modeling, end-users, stakeholders, experts, and entrepreneurs are able to create a shared understanding of a system representation. While the Unified Modeling Language (UML) is one of the major conceptual modeling languages in object-oriented software engineering, more and more concerns arise from the modeling quality of UML and its tool-support. Among them, the limitation of the two-dimensional presentation of its notations and lack of natural collaborative modeling tools are reported to be significant. In this paper, we explore the potential of using virtual reality (VR) technology for collaborative UML software design by comparing it with classical collaborative software design using conventional devices (desktop PC/laptop). For this purpose, we have developed a VR modeling environment that offers a natural collaborative modeling experience for UML Class Diagrams. Based on a user study with 24 participants, we have compared collaborative VR modeling with conventional modeling with regard to efficiency, effectiveness, and user satisfaction. Results show that the use of VR has some disadvantages concerning efficiency and effectiveness, but the user's fun, the feeling of being in the same room with a remote collaborator, and the naturalness of collaboration were increased.

Towards Verifying UML Class Diagram and Formalizing Generalization/Specialization Relationship with Mathematical Set Theory

The Unified Modeling Language (UML) is considered the de facto standard for object-oriented software model development. This makes it appropriate to be used in academia courses at both the graduate and undergraduate levels of education. Some challenges to using the UML is academia are its large number of model concepts and the imprecise semantic of some of these concepts. These challenges are daunting for students who are being introduced to the UML. One approach that can be taken in teaching UML towards addressing these concerns is to limit the number of UML concepts taught and recognize that students may not be able to develop correct UML system models. This approach leads to research work that develop a limited set of UML model concepts that are fewer in number and have more precise semantics. In this paper, we present a new approach to resolve an aspect of this problem by simplifying the generalization/specialization semantics of the class diagram through the application of mathematical formality to usage of these class diagram concepts. Along with that, we discuss the progress of research in the area of verification of UML class models. This research work derives a core set of concepts suitable for graduate and undergraduate comprehension of UML modeling and defines more precise semantics for those modeling concepts. The applicable mathematical principles applied in this work are from the domains of set theory and predicate logic. This approach is particularly relevant for the pedagogy of software engineering and the development of software systems that require a high level of reliability.

Characterizing Student Proficiency in Software Modeling in Terms of Functions, Structures, and Behaviors

Software modeling is an integral practice for software engineers, especially as the complexity of software solutions increases. Unified Modeling Language (UML) is the industry standard for software modeling. however, it is often used incorrectly and misunderstood by novice software designers. This study is centered around understanding patterns of student proficiency of abstraction and systems thinking within a software modeling context. The participants of this study (n = 97) belonged to a systems analysis and design course that is primarily taken by second-year university students. The exam solutions to a case study from the course were evaluated for modeling proficiency. As evidence of proficiency in abstract thinking and systems thinking, we evaluated UML activity diagrams, class diagrams, and sequence diagrams and the alignment between these representations in terms of functions, structures, and behaviors. The results suggest students being proficient in modeling the functional aspects of an information system while facing some difficulty in capturing the structural and behavioral aspects of an information system. Clustering analysis revealed two groups within the sample, with one group displaying a significantly higher abstraction and systems thinking ability. Statistically significant correlations were also found between student proficiency of abstraction and their modeling proficiency in terms of functions, structures, and behaviors.

Automatic Generation of Object-Oriented Code from the ReLEL Requirements Model

The final executable code should no longer be considered as a central element in a software development process but rather a naturally important component that results from a model transformation. The objective of the MDA (Model Driven Architecture) approach is to lift the lock of software development automation from the CIM (Computation Independent Model) requirements until the code of an application is obtained. Therefore, we have proposed in the framework of MDA an approach that consists of automatically generating object-oriented code from the CIM model represented by ReLEL (Restructuring extended Lexical Elaborate Language). ReLEL is a natural language-oriented model that represents both the client requirements and the conceptual level of a system. However, the MDA framework does not recommend the type of UML model that corresponds to each business activity. Consequently, automating the software development process from the CIM model specified by ReLEL becomes a complex task. Our strategy in this paper includes the instantiation of the ReLEL model in the Praxeme methodology, which models each of the company's concerns, grouped in a homogeneous whole, using the UML (Unified Modeling Language) and which considers the articulation of these aspects by adopting the MDA principle. To do this, we propose to automate the articulation that covers the intentional, semantic, logical, and software aspects of Praxeme. To validate our approach, we measure the coupling and cohesion of the UML class diagram obtained from the Java code generated from this article using the slicing technique. The results show that the coupling is weak, and the cohesion is strong. It can be deduced that the method proposed in this paper can produce a more reliable and efficient system.

A Tool to Automate Student UML diagram Evaluation

Unified modelling language (UML) is the accepted standard and modelling language for modeling in software development process. UML is widely used by most course tutors in teaching modules of software engineering and system analysis and design. Students taking such courses do submit assignments with UML diagrams such as use case, class, sequence, activity and so on. Different versions of such diagrams produced by the students for a given problem have to be assessed by the course tutor which is a challenging and time-consuming task. This paper presents a java-based tool which is developed based on a simple yet effective algorithm developed by the authors that will read student and tutors solution diagrams as inputs and evaluate and grade the diagrams automatically. The output of the tool is the score of the student diagram in respect of lecturer’s final solution. The output is presented in two feedback files, one containing students’ score for the lecturers and the other to be send to the student to note the areas that were incorrect. The tool has been tested and evaluated using a simple and assumed UML class diagram. The result shows that the tool functions effectively and can produce detail feedbacks for both students and tutors. The outcome of this paper contributes towards automating UML diagram evaluations.

Automatic Classification of UML Class Diagrams Using Deep Learning Technique: Convolutional Neural Network

Unified Modeling Language (UML) includes various types of diagrams that help to study, analyze, document, design, or develop any software efficiently. Therefore, UML diagrams are of great advantage for researchers, software developers, and academicians. Class diagrams are the most widely used UML diagrams for this purpose. Despite its recognition as a standard modeling language for Object-Oriented software, it is difficult to learn. Although there exist repositories that aids the users with the collection of UML diagrams, there is still much more to explore and develop in this domain. The objective of our research was to develop a tool that can automatically classify the images as UML class diagrams and non-UML class diagrams. Earlier research used Machine Learning techniques for classifying class diagrams. Thus, they are required to identify image features and investigate the impact of these features on the UML class diagrams classification problem. We developed a new approach for automatically classifying class diagrams using the approach of Convolutional Neural Network under the domain of Deep Learning. We have applied the code on Convolutional Neural Networks with and without the Regularization technique. Our tool receives JPEG/PNG/GIF/TIFF images as input and predicts whether it is a UML class diagram image or not. There is no need to tag images of class diagrams as UML class diagrams in our dataset.

MODEL OF INFORMATION PLATFORM FOR ELECTRICITY DISTRIBUTION SYSTEMS PLANNING

Background. Implementation of the Smart Grid concept in Ukraine is under consideration, and it should become an integral part of the electricity distribution systems planning. One of the outstanding issues is exchanging the information on the distribution systems development between all stakeholders using modern information and communication solutions. Objective. The purpose of the research is to develop a generalized model of information platform for electricity distribution system planning and expansion of Smart Grid technologies in the energy system of Ukraine. Methods. The experience of the European Union (EU) in electricity distribution system planning, and specifically, the organization of interaction between stakeholders was analyzed. With the help of a system approach, the requirements of the existing in Ukraine laws and regulations on electricity distribution system planning were investigated. The model structure of the proposed information platform was formalized by the graphical tools of the unified modeling language (UML). Results. It is determined that the EU organizes the information exchange for the purposes of electricity distribution system planning, using different tools in parallel: specialized institutions for communication of stakeholders; cooperation of energy regulators; reports of scientific institutions about Smart Grid project implementation; information technology platforms for modeling the electricity distribution system in cooperation with its users. The existing in Ukraine scheme of interaction between all actors directly or indirectly involved in electricity distribution system planning was investigated; it was proposed to develop an information technology platform “Ukrainian Smart Energy – USE” to eliminate the identified negative aspects of information exchange between the actors. The generalized structure of the USE platform was formalized using a UML class diagram; it shows the content of data available for viewing and required for publication by different classes of the platform users, depending on their functions. Conclusions. The proposed model of information platform USE is the basis for further investigations directed to develop an appropriate information technology platform.

Aligning Software System Level with Business Process Level through Model-Driven Architecture

Information systems are intended to provide organisations with a new way of sustaining themselves, by helping them manage their activities using innovative technologies. Information systems require aligned levels for maximum effectiveness. In this context, business and information technology (IT) alignment is an important issue for the success of organisations. This paper presents the first step of the proposed approach to align the software system level, modelled by a Unified Modeling Language (UML) class diagram, with the business process level, modelled by the Business Process Model and Notation (BPMN) model. A model-driven architecture approach is proposed as a means to transform a set of BPMN models into a UML class diagram. A set of transformation rules is proposed, followed by guidelines that help apply those rules.

Modeling a Competence-based Industry Role Selection System for University Graduates Using Machine Learning

This paper presents a design of a system for industry role selection, representing both its structure and behavior. Knowing the right industry role that suits a graduate based on their competences on graduation has remained a critical matter for graduates when searching for jobs after graduation. Thousands of university students graduate each year and enter the market to search for jobs that are limited. Searching without prior information on the most appropriate industry role one is suitable for leads to blind search. Blind search not only puts graduates at risk of long-term unemployment and job mismatch but also overloads employers with many applications during job selection. Therefore, this paper addresses 2 objectives: 1) to model the system’s structure, and 2) to design the algorithm for the system’s behavior. Since object-oriented programming is currently the dominant programming paradigm, object modeling technique was selected to model both the system’s structure and the algorithm for the system’s behavior. To realize object modeling and represent the system’s artifacts in a highly simplified form, Unified Modeling Language (UML) was adopted as the standard modeling toolkit. More specifically, UML class diagram was used to represent the structural model of the system where the underlying objects of the model were exactly similar to those of the problem domain. Finally, use case diagram of the UML toolkit was used to represent the system’s behavior in selecting industry role for graduates. To ensure that the system improves performance of its behavior through experience in selecting industry roles for graduates, Machine Learning (ML) algorithm was designed. Two machine learning techniques, naïve Bayes and Support Vector Machines (SVM), were used as the algorithm’s criteria for selecting industry roles for graduates. Experiments to evaluate performance of the system were conducted using data collected from Software Engineering industry domain. The end product was design of an intelligent industry role selection system with relevant structure and behavior to easily work with both in the academia and industry. Findings reveal the system improves performance of its behavior in selecting industry roles for graduates much better under SVM (67%) than naïve Bayes (57%). On the same benchmark dataset, the system recorded better performance (85%) than reported performance (82%) in the benchmark system. These findings will benefit industry by getting evaluation tool for revealing graduate’s suitability for employment which they can use as prior information for decision making when filtering candidates for interview. Besides, this will provide researchers with a digital platform to study and bridge the gap between industry and academia. Lastly, this will attempt to reduce both low job satisfaction and long-term unemployment that is one of the causes of social and economic pain both in Kenya and around the world. This paper has revealed competence based industry role selection system with relevant structure and behavior can improve searching of jobs by providing a fairly accurate prior information. However, this paper recommends testing this approach with other alternative machine learning techniques as well as other alternative industry domains.

Object-Based Similarity Assessment Using Land Cover Meta-Language (LCML): Concept, Challenges, and Implementation

Land cover (LC) is an essential variable for environmental monitoring in many application domains. The detection of changes in LC can support the understanding of environmental dynamics. However, LC legends present a high degree of inconsistencies that significantly reduce their usability. This study investigates the effectiveness of ISO standard 19144-2, better known as Land Cover Meta-Language (LCML), to improve the standardization and harmonization of different LC taxonomies and maps. LCML vocabulary and syntactic rules facilitate the integration of natural resources information. LC classes are represented by a sequence of “Basic Elements” and attributes defined as “Properties” and “Characteristics.” Such elements are formalized in a Unified Modeling Language class diagram. This study presents first, a method to evaluate and score the “similarity” of different LCML legends, second, an application of the similarity assessment criteria to an area located in Bangladesh for translating its specific LCML legend into a different taxonomy, i.e., the System of Environmental Economic Accounting, and third, a Python implementation to be incorporated in new or already existing tools. The results obtained show that when class similarity assessment is carried out by Basic Elements only, the process performs well for simple classes. When classes are characterized by similar basic elements (e.g., biotic elements) structure, the introduction of class properties is needed to disambiguate complex situations. The findings indicate that the proposed methodology can exploit LCML land feature semantic representation. Moreover, it can be used for translating LCML classes into different taxonomies, for facilitating class comparison and change detection.

Automatic extraction of product line architecture and feature models from UML class diagram variants

Abstract Context Software Product Lines (SPLs) are families of related products developed for specific domains. SPLs commonly emerge from existing variants when their individual maintenance and/or evolution become complex. Even though there exists a vast research literature on SPL extraction, the majority of the approaches have only focused on source code, are partially automated, or do not reflect domain constraints. Such limitations can make more difficult the extraction, management, documentation and generation of some important SPL artifacts such as the product line architecture, a fact that can impact negatively the evolution and maintenance of SPLs. Objective To tackle these limitations, this work presents ModelVars2SPL (Model Variants to SPL Core Assets), an automated approach to aid the development of SPLs from existing system variants. Method The input for ModelVars2SPL is a set of Unified Modeling Language (UML) class diagrams and the list of features they implement. The approach extracts two main assets: (i) Feature Model (FM), which represents the combinations of features, and (ii) a Product Line Architecture (PLA), which represents a global structure of the variants. ModelVars2SPL is composed of four automated steps. We conducted a thorough evaluation of ModelVars2SPL to analyze the artefacts it generates and its performance. Results The results show that the FMs well-represent the features organization, providing useful information to define and manage commonalities and variabilities. The PLAs show a global structure of current variants, facilitating the understanding of existing implementations of all variants. Conclusions An advantage of ModelVars2SPL is to exploit the use of UML design models, that is, it is independent of the programming language, and supports the re-engineering process in the design level, allowing practitioners to have a broader view of the SPL.

How are UML class diagrams built in practice? A usability study of two UML tools: Magicdraw and Papyrus

Software modeling is a key activity in software development, especially when following any kind of Model Driven Software Engineering (MDSE) process. In this context, standard modeling languages, like the Unified Modeling Language (UML), and tools for supporting the modeling activities become essential.The aim of this study is to analyze how modelers build UML models and how good modeling tools are in supporting this task. Our goal is to draw some useful lessons that help to improve the (UML) modeling process both by recommending changes on the tools themselves and on how UML is taught so that theory and practice of UML modeling are better aligned.Our study employs two research approaches. The main one is an empirical experiment (which analyzes screen recordings registered by undergraduate students during the construction of a UML class diagram). An analytical analysis complements the previous experiment. The study focuses on the most frequent type of UML diagram, the class diagram, and on two tools widely used by the modeling community: MagicDraw and Papyrus.

Using the Object Mapping Approach from Analysis to Implementation for Developing Student Registration System

<table width="593" border="1" cellspacing="0" cellpadding="0"><tbody><tr><td valign="top" width="387"><p>The Unified Modeling Language (UML) is the effective standard language of an object-oriented based system analysis and design. The using of object-oriented approach based on UML specification can offer an understandable model, which can reduce the system's complexity. The use-case diagram, class diagram and sequence diagram are important models of the system development during early stages. The object mapping approach between UML class diagram in the analysis phase and source code in the implementation phase is a significant process in the software development life cycle. In order to ensure the consistency of system requirements from analysis to implementation. This paper presents the student registration system by using object mapping based on UML. The UML specification and the generation of Java source code influenced by specific features of class in object-oriented such as inheritance and the association between the classes.</p></td></tr></tbody></table>

Automatic concept identification of software requirements in Turkish

Software requirements include description of the features for the target system and express the expectations of users. In the analysis phase, requirements are transformed into easy-to-understand conceptual models that facilitate communication between stakeholders. Although creating conceptual models using requirements is mostly implemented manually by analysts, the number of models that automate this process has increased recently. Most of the models and tools are developed to analyze requirements in English, and there is no study for agglutinative languages such as Turkish or Finnish. In this study, we propose an automatic concept identification model that transforms Turkish requirements into Unified Modeling Language class diagrams to ease the work of individuals on the software team and reduce the cost of software projects. The proposed work is based on natural language processing techniques and a new rule-set containing twenty-six rules is created to find object-oriented design elements from requirements. Since there is no publicly available dataset on the online repositories, we have created a well-defined dataset containing twenty software requirements in Turkish and have made it publicly available on GitHub to be used by other researchers. We also propose a novel evaluation model based on an analytical hierarchy process that considers the experts' views and calculate the performance of the overall system as 89 %. We can state that this result is promising for future works in this domain.

Analysis of the AnyWalker Software Architecture Using the UML Refactoring Tool

Refactoring (restructuring of a system, which keeps its functionality invariant) of the software can be aimed at the optimization of such characteristics as modifiability, maintainability and understandability. Model refactoring can be conducted not only at the code level, but also at the model level. According to the MDA approach models are described using the Unified Modelling Language (UML). UML class diagrams are used to specify software architecture. UML Refactoring is a software tool, which provides UML class diagram analysis and refactoring. This article proposes results of AnyWalker project UML class diagram refactoring using the UML Refactoring tool.

An UML to OWL based approach for extracting Moodle’s Ontology for Social Network Analysis

The main analyses in Social Network Analysis field are focusing on measuring the frequency of social interactions that are modeled only with numbers; 0 or 1 on the graph adjacency matrix. Therefore, ontologies have drawn attention to empower and enhance the Social Network Analysis with semantics. By exploring theses semantics, besides we better view and analyze the interactions between students and teachers inside the Learning Management System, the network graph structure and analyses change. In this paper, we propose a methodology to build Moodle’s OWL (Web Ontology Language) ontology for Social Network Analysis ends. The proposed ontology gives explicit meaning to the relationships inside Moodle instead of modeling them as nodes and edges with implicit semantics like the case in classical Social Network Analysis. The semantics of relationships influences the graph structure and statistics by making some nodes more central and influential and other nodes less central and influential. The built ontology is basically made by exploring the UML (Unified Modeling Language) class diagram that we created from Moodle Mount Orange School and is our main metadata source.