UML 클래스 다이어그램 분석에 의한 객체지향 시스템의 복잡도 연구

Qualities of software design heavily affect on qualities of software ultimately developed. One of the claimed advantages of object-oriented paradigm is the ease of maintenance. Assessing maintainability in design phase will help software designers to decide that the design of software should be altered or not. This will help reducing cost of software maintenance in later phases. This research presents a controlled experiment carried out to investigate whether structural complexity and aesthetic metrics can be indicators of class and sequence diagrams maintainability and to establish maintainability models from both metric sets. Maintainability models are constructed by using three techniques: Discriminant analysis, Decision tree and MLP neural network in order to find the best one among these models. The preliminary result shows that the maintainability models constructed by applying MLP neural network are the best models in terms of the model accuracy. Moreover, this research constructs an automated tool for predicting maintainability using the maintainability models and also proposes a new set of structural complexity metrics for measuring maintainability from class diagrams.

We report the preliminary results of a controlled experiment conducted to analyze whether the combined use of UML class and sequence diagrams better supports source code comprehension with respect to the use of class and sequence diagrams alone. We also investigated which notation between class and sequence diagrams provides a better support in the execution of comprehension tasks on source code. The results suggest that it is better to use class and sequence diagrams together with respect to using either class or sequence diagrams alone. The difference in the source code comprehension is statistically significant with respect to the use of class diagrams alone, while is not statistically significant with respect to the sequence diagrams alone.

A novel metric for assessing structural complexity of data warehouse requirements models

Recently, reverse engineering has become widely recognized as a valuable process for extracting system abstractions and design information from existing software. This study focuses on ForUML, a reverse engineering tool developed to extract UML diagrams from modern object-oriented Fortran programs. Generally, Fortran is used to implement scientific and engineering software in various domains, such as weather forecasting, astrophysics, and engineering design. However, methods for visualizing the existing design of object-oriented Fortran software are lacking. UML diagrams of Fortran software would be beneficial to scientists and engineers in explaining the structure and behavior of their programs at a higher level of abstraction than the source code itself. UML diagrams can enhance discussions within development teams and with the broader scientific community. The first version of ForUML produces only UML class diagrams. Class diagrams provide a useful window into the static structure of a program, including the structure and components of each class and the relationships between classes. However, class diagrams lack the temporal information required to understand class behavior and interactions between classes. UML sequence diagrams provide this important algorithmic information. Therefore, herein, an extension for ForUML to extract UML sequence diagrams from the Fortran code is proposed, and this capability is provided using a widely used open-source platform. This study argues that the proposed extension will enable the visualization of object-oriented Fortran software behavior and algorithmic structure and thereby enhance the development, maintenance practices, decision processes, and communications in scientific and engineering software communities worldwide.

Corals provide a complex 3D framework that offers habitat to diverse coral reef fauna. However, future reefs are likely to experience reduced coral abundance. Sponges have been proposed as one potential winner on future coral reefs, but little is known of how they contribute to reef 3D structure. Given the ecological importance of structural complexity, it is critical to understand how changes in the abundance of structure-building organisms will affect the three-dimensional properties of coral reefs. To investigate the potentially important functional role of coral reef sponges as providers of structural complexity, we compared the structural complexity of coral- and sponge-dominated areas of an Indonesian coral reef, using 3D photogrammetry at a 4 m2 spatial scale. Structural complexity of 31 4 m2 quadrats was expressed as rugosity indicating reef contour complexity (R), vector dispersion indicating heterogeneity of angles between reef surfaces (1/k), and fractal dimension indicating geometrical complexity at five different spatial scales between 1 and 120 cm (D1–5). Quadrats were identified as high- or low-complexity using hierarchical clustering based on the complexity metrics. At high structural complexity, coral- and sponge-dominated quadrats were similar in terms of R and 1/k. However, smallest-scale refuge spaces (1–5 cm) were more abundant in coral-dominated quadrats, whereas larger scale refuge spaces (30–60 cm) were more abundant in sponge-dominated quadrats. Branching and massive corals contributed the most to structural complexity in coral-dominated quadrats, and barrel sponges in sponge-dominated quadrats. We show that smaller-scale refugia (1–5 cm) are reduced on sponge-dominated reefs at the spatial scale considered here (4 m2), with potential negative implications for smaller reef fauna.

In the process of designing, manufacturing and operating complex technological systems, it is necessary to have criteria and methods for assessing the perfection of their structure. A method for assessing the complexity of the structure of technological systems by the criterion that is a complex quantity and takes into account the number of system elements, connections between elements, connections between elements and the external environment, and the hierarchical level of elements in the system is developed. The choice of the criterion was made under the following assumptions: the element of the system has one «input» and one «output», through which its interaction with other elements of the system and the external environment occurs; the state of the system element is uniquely determined by the state of its «input». The complexity of the structure of the unit element is unity; the complexity of the structure of the system consisting of an infinite number of elements is equal to infinity. The complexity of the system structure is determined by the number of system elements, the number of connections between them and the external environment, and also the hierarchical level of the element in the system. The method for assessing the complexity of the structure of technological systems is sufficiently accurate, intuitively acceptable and suitable for practical research. Application of the developed method in practice allows solving the problem of objective analysis of the structure of complex technological systems and giving them a quantitative assessment. An example of using the method for analyzing the structure of technological equipment systems of sintering plants of the Krivoy Rog iron ore basin (Ukraine) is given.

Quality of data models for data warehouse has significant effect on the quality of data warehouse. Complexity metrics play significant role in predicting quality attributes of a software artifact. Few researchers have proposed structural complexity metrics for the multidimensional data models for data warehouse which may act as objective indicators of the quality of these models. However, the metrics proposed earlier have not considered the structural complexity due to relationships among various elements present in these models. This paper proposes a complexity metric which considers structural complexity due to relationships among elements present in multidimensional models for data warehouse. The metric is proposed on the basis of Goal Question Metric approach. The practical usefulness of the proposed metric is proved by validating the metric using a practical framework proposed by Kaner. This preliminary validation suggests that the metric may be linked to the quality of the multidimensional models. The advantage of the metric is that it is available during early phase of software development life cycle. The metric will also help the developers to select quality data model among various semantically equivalent models.

A relevant goal of software engineering is to assure the quality of object oriented software from the conceptual modeling phase. UML Class diagrams constitute a key artifact at that stage. Among existing models for iterative and incremental development of software systems, Model Driven Architecture (MDA) has reached a leadership position. MDA enables model-driven software development which treats models as primary development artifacts. The present empirical study answers the following three research questions: (RQ1) are there available in the literature class complexity metrics that can be adopted at the business level? (RQ2) Is it possible to adopt those metrics (if any) to predict the quality of the code returned by xGenerator? The latter is a Java technology platform for the creation of MVC Web applications, which implements the model-driven approach. (RQ3) Is it possible to identify a threshold for the adopted metrics (if any) that might suggest when a business Class diagram should be refactored?

This paper is aimed to describe the structural complexity of the traffic light control system. The structural complexity represents an interrelationship indicator between the various elements functions. The traffic light control system has several additional features that are synchronized to the railway doorstop, and the time interval for the green signals is expanded. For determining the method of its structural complexity, the Petri net model is used. Three kinds of traffic lights models are explored, namely standard, Norwegian, and the Norwegian improvement. The study results indicate that the Norwegian traffic lights have the most complex structure when regular implementation. The runner up is the Norwegian improvement. The most ideal is the standard traffic lights. Norwegian improvement is the most ideal when integrated with the railway doorstop and implemented extra green signals time interval. The Norwegian traffic light is the second. The standard traffic lights structural complexity fluctuates. It means that the Norwegian traffic light and its improvement are suitably used. Adding several features improve the structural complexity approaching the ideal system while extra controllers accompany each element.
 HIGHLIGHTS
 
 The Petri net model can represent the structural complexity of a system
 The method that can measure the structural complexity becomes ideal for actual implementation
 The Norwegian traffic lights structural complexities and improvements are ideal for the systems synchronized to the railway and appropriate for systems using the extra green signal time intervals or other additional features
 
 GRAPHICAL ABSTRACT

This study evaluates the potential of low-altitude airborne laser scanning (ALS) and terrestrial laser scanning (TLS) for characterizing structural complexity in Southern Finland. Unlike species diversity, structural complexity reflects realized niche occupancy by describing how vegetation utilizes light, water, and space, providing key insights into ecosystem functioning. We analyzed 99 circular sample plots (r = 20 m) scanned with helicopter-borne ALS at 80 m altitude and TLS data from nine scan locations per plot. Structural complexity metrics were derived at both grid level (variability in canopy height models and voxel occupancy) and object level (variability in individual tree attributes). High-density ALS effectively captured vertical and horizontal complexity through object-level analysis, showing close agreement with TLS. However, differences in measurement geometry affected volumetric complexity, with ALS and TLS characterizing tree architecture and vegetation occupancy differently. Object-level approaches captured a broader range of horizontal and vertical complexity, while grid-level approaches better captured volumetric variability, facilitating the identification of forest stand properties and biodiversity hotspots. The strongest agreement between ALS and TLS occurred for variation in tree height (R² = 0.66, Spearman = 0.80), while lowest agreement was found for fractal dimensions of tree architecture (R² = 0.04, Spearman = 0.25).

<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>

Size estimation is one of important processes related to success of software project management. This paper presents novel software size estimation model by using Multilayer Perceptron approach. Software size in terms of Lines of code is used as criterion variable. Structural complexity metrics are used as predictors. The metrics can be captured from a software design model named UML Class diagram. A high predictive ability of the model is shown with correlation coefficient measure. Moreover, four training algorithms; Levenberg-Marquardt, Scaled Conjugate Gradient, Broyden-Fletcher-Golfarb-Shanno and Bayesian Regularization, have been applied on the network for better estimation. The obtained results indicate the highest accuracy on the model with Bayesian Regularization algorithm.

Interface and interaction design take up most of the times in software creation. Human Computer Interaction (HCI) is mainly the best way to interact with a computer. During software development, class diagram is the only required step and process to design an information system according to UML notation. In addition, creating class diagram is a mandatory activity during the software creation process. The aim of this paper is to automatically generate the HCI mockup from class diagram to make easy and extremely fast software design. For example, the SEF (Schéma d'Enchaînement de Fenêtre), is an interface model allowing to create a new interface mockup. It offers more widgets to design the HCI. And MACAO (Méthode d'Analyse et de Conception d'Applications Orientées-objets) is one of the methods which helps computer scientist to easily create software. The model transformation is based on Model Driven Engineering (MDE). We use UML notation to design the class diagrams. We also adopt MDA architecture to create the model-based transformation process. Transformation rules are created using Atlas Transformation Language (ATL) to automatically generate a specific interface from the class diagram.

Structural complexity refers to the three-dimensional arrangement and variability of both biotic and abiotic components of an ecosystem. Metrics that characterize structural complexity are often used to manage various aspects of ecosystem function, such as light transmittance, wildlife habitat, and biological diversity. Additionally, these metrics aid in evaluating resilience to disturbance events, including hurricanes, bark-beetle outbreaks, and wildfire. Recent advances in wildland fire modelling have facilitated the integration of forest structural complexity metrics into the QUIC-Fire model, enabling real-time prediction of fire spread and behaviour by simulating interactions between fire, weather, topography, and forest structure. While QUIC-Fire is designed to be highly adaptable, model performance depends on the availability and accuracy of local data inputs. Expanding the model's usability across different regions can be facilitated by the availability of more comprehensive and high-quality data. Thus, the primary goal behind the data products we developed was to establish a basis for collaborative research across various disciplines, particularly within the focal areas of the Southern Research Station, such as forestry, wildland fire, hydrology, soil science, and cultural resources at Bent Creek, Coweeta, Escambia, and Hitchiti Experimental Forests (EFs).Airborne laser scanning (ALS) was used to collect point-cloud data for each EF during the leaf-off season to minimize interference from foliage. Subsequent processing of the raw lidar data involved outlier detection and filtering, ground and non-ground classification, and the computation of a variety of metrics representing various aspects of topography and forest structure at both the pixel-level and the tree-level. Pixel-level topographic data products include: digital elevation model (DEM), slope, aspect, topographic position index (TPI), topographic roughness index (TRI), roughness, and flow direction. Forest structural-complexity metrics include canopy height, foliar height diversity (FHD), vertical distribution ratio (VDR), canopy rugosity, crown relief ratio (CRR), understory complexity index (UCI), vertical complexity index (VCI), canopy cover, mean vegetation height, and the standard deviation of vegetation height. Tree-level data products were computed from the point cloud using multiple algorithms to perform individual tree detection (ITD) and individual tree segmentation (ITS). The datasets have been harmonized and are openly accessible through the USDA Forest Service Research Data Archive.

Distinct zonation of community assemblages among habitats is a ubiquitous feature of coral reefs. The distribution of roving herbivorous fishes (parrotfishes, surgeonfishes and rabbitfishes) is a particularly clear example, with the abundance of these fishes generally peaking in shallow-water, high-energy habitats, regardless of the biogeographic realm. Yet, our understanding of the factors which structure this habitat partitioning, especially with regards to different facets of structural complexity and nutritional resource availability, is limited. To address this issue, we used three-dimensional photogrammetry and structure-from-motion technologies to describe five components of structural complexity (rugosity, coral cover, verticality, refuge density and field-of-view) and nutritional resource availability (grazing surface area) among habitats and considered how these factors are related to herbivorous fish distributions. All complexity metrics (including coral cover) were highest on the slope and crest. Nutritional resource availability differed from this general pattern and peaked on the outer-flat. Unexpectedly, when compared to the distribution of herbivorous fishes, none of the complexity metrics had a marked influence in the models. However, grazing surface area was a strong predictor of both the abundance and biomass of herbivorous fishes. The strong relationship between grazing surface area and herbivorous fish distributions indicates that nutritional resource availability may be one of the primary factors driving the distribution of roving herbivorous fishes. The lack of a relationship between complexity and herbivorous fishes, and a strong affinity of herbivorous fishes for low-complexity, algal turf-dominated outer-flat habitats, offers some cautious optimism that herbivory may be sustained on future, low-complexity, algal turf-dominated reef configurations.