scholarly journals Measuring Software Modularity Based on Software Networks

Entropy ◽  
2019 ◽  
Vol 21 (4) ◽  
pp. 344 ◽  
Author(s):  
Yiming Xiang ◽  
Weifeng Pan ◽  
Haibo Jiang ◽  
Yunfang Zhu ◽  
Hao Li
Keyword(s):  
Complex Systems ◽  
Complex Network ◽  
Software Metrics ◽  
Empirical Evaluation ◽  
Network Models ◽  
Software Systems ◽  
Complex Network Theory ◽  
Software Metric ◽  
Software Network ◽  
Software Modularity

Modularity has been regarded as one of the most important properties of a successful software design. It has significant impact on many external quality attributes such as reusability, maintainability, and understandability. Thus, proposing metrics to measure the software modularity can be very useful. Although several metrics have been proposed to characterize some modularity-related attributes, they fail to characterize software modularity as a whole. A complex network uses network models to abstract the internal structure of complex systems, providing a general way to analyze complex systems as a whole. In this paper, we introduce the complex network theory into software engineering and employ modularity, a metric widely used in the field of community detection in complex network research, to measure software modularity as a whole. First, a specific piece of software is represented by a software network, feature coupling network (FCN), where methods and attributes are nodes, couplings between methods and attributes are edges, and the weight on the edges denotes the coupling strength. Then, modularity is applied to the FCN to measure software modularity. We apply the Weyuker’s criteria which is widely used in the field of software metrics, to validate the modularity as a software metric theoretically, and also perform an empirical evaluation using open-source Java software systems to show its effectiveness as a software metric to measure software modularity.

Deep learning and complex network theory based analysis on socialized manufacturing resources utilisations and an application case study

2021 ◽  
pp. 1063293X2110031
Author(s):  
Maolin Yang ◽  
Auwal H Abubakar ◽  
Pingyu Jiang
Keyword(s):  
Deep Learning ◽  
Complex Network ◽  
Network Models ◽  
Neural Network Models ◽  
Complex Network Theory ◽  
New Type ◽  
Manufacturing Resource ◽  
Manufacturing Resources ◽  
Resource Characteristics

Social manufacturing is characterized by its capability of utilizing socialized manufacturing resources to achieve value adding. Recently, a new type of social manufacturing pattern emerges and shows potential for core factories to improve their limited manufacturing capabilities by utilizing the resources from outside socialized manufacturing resource communities. However, the core factories need to analyze the resource characteristics of the socialized resource communities before making operation plans, and this is challenging due to the unaffiliated and self-driven characteristics of the resource providers in socialized resource communities. In this paper, a deep learning and complex network based approach is established to address this challenge by using socialized designer community for demonstration. Firstly, convolutional neural network models are trained to identify the design resource characteristics of each socialized designer in designer community according to the interaction texts posted by the socialized designer on internet platforms. During the process, an iterative dataset labelling method is established to reduce the time cost for training set labelling. Secondly, complex networks are used to model the design resource characteristics of the community according to the resource characteristics of all the socialized designers in the community. Two real communities from RepRap 3D printer project are used as case study.

scholarly journals The South African Banking Director Network: An Investigation Into Interlocking Directorships Using Social Network Analysis (SNA)

2014 ◽  
Vol 13 (5) ◽  
pp. 963
Author(s):  
Burgert A. Senekal ◽  
Karlien Stemmet
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Complex Systems ◽  
South African ◽  
Complex Network ◽  
Network Theory ◽  
Complex Network Theory ◽  
International Trends ◽  
Director Network

The theory of complex systems has gained significant ground in recent years, and with it, complex network theory has become an essential approach to complex systems. This study follows international trends in examining the interlocking South African bank director network using social network analysis (SNA), which is shown to be a highly connected social network that has ties to many South African industries, including healthcare, mining, and education. The most highly connected directors and companies are identified, along with those that are most central to the network, and those that serve important bridging functions in facilitating network coherence. As this study is exploratory, numerous suggestions are also made for further research.

Application of Complex Network Theory in Computer Network Topology Optimization Research

2014 ◽  
Vol 989-994 ◽  
pp. 4237-4240
Author(s):  
Zhi Kun Wang
Keyword(s):  
Complex Systems ◽  
Complex Networks ◽  
Complex Network ◽  
Network Topology ◽  
Computer Network ◽  
Transportation Network ◽  
Power Grids ◽  
Research Network ◽  
Complex Network Theory ◽  
New Research

If we apply the system internal elements as nodes, and the relationship between the elements as connection, then the system form a network. If we put emphasis on the structure of the system and analyze the function of the system from the angle of structure, we’ll find that real network topology properties differ from previous research network, and has numerous nodes, which is called complex networks. In the real word, many complex systems can be basically described by the network, while the reality is that complex systems can be called as “complex network”, such as social network, transportation network, power grids and internet etc. In recent years, many articles about the complex networks are released in the international first-class publications such as Nature, PRL, PNAS, which reflects that the complex networks has become a new research focus.

scholarly journals Recent Progress about Flight Delay under Complex Network

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-18
Author(s):  
Tang Zhixing ◽  
Huang Shan ◽  
Han Songchen
Keyword(s):  
Complex Network ◽  
Recent Progress ◽  
Model Building ◽  
Prediction Models ◽  
Transportation Network ◽  
Network Models ◽  
Air Transportation ◽  
Traffic Information ◽  
Complex Network Theory ◽  
Flight Delay

Flight delay is one of the most challenging threats to operation of air transportation network system. Complex network was introduced into research studies on flight delays due to its low complexity, high flexibility in model building, and accurate explanation about real world. We surveyed recent progress about flight delay which makes extensive use of complex network theory in this paper. We scanned analyses on static network and temporal evolution, together with identification about topologically important nodes/edges. And, we made a clarification about relations among robustness, vulnerability, and resilience in air transportation networks. Then, we investigated studies on causal relations, propagation modellings, and best spreaders identifications in flight delay. Ultimately, future improvements are summarized in fourfold. (1) Under Complex Network, flight operation relevant subsystems or sublayers are discarded by the majority of available network models. Hierarchical modelling approaches may be able to improve this and provide more capable network models for flight delay. (2) Traffic information is the key to narrow the gap between topology and functionality in current situations. Flight schedule and flight plan could be employed to detect flight delay causalities and model flight delay propagations more accurately. Real flight data may be utilized to validate and revise the detection and prediction models. (3) It is of great importance to explore how to predict flight delay propagations and identify best spreaders at a low cost of calculation complexity. This may be achieved by analyzing flight delay in frequency domain instead of time domain. (4) Summation of most critical nodes/edges may not be the most crucial group to network resilience or flight delay propagations. Effective algorithm for most influential sequence is to be developed.

scholarly journals Impact of Network Topology on the Spread of Infectious Diseases

2020 ◽  
Vol 21 (1) ◽  
pp. 95
Author(s):  
Eduardo R. Pinto ◽  
Erivelton G. Nepomuceno ◽  
Andriana S. L. O. Campanharo
Keyword(s):  
Infectious Disease ◽  
Complex Network ◽  
Network Models ◽  
Vaccination Strategy ◽  
Small World ◽  
Disease Spread ◽  
Complex Network Theory ◽  
General Complex ◽  
The Individual ◽  
Complex Network Models

The complex network theory constitutes a natural support for the study of a disease propagation. In this work, we present a study of an infectious disease spread with the use of this theory in combination with the Individual Based Model. More specifically, we use several complex network models widely known in the literature to verify their topological effects in the propagation of the disease. In general, complex networks with different properties result in curves of infected individuals with different behaviors, and thus, the growth of a given disease is highly sensitive to the network model used. The disease eradication is observed when the vaccination strategy of 10% of the population is used in combination with the random, small world or modular network models, which opens an important space for control actions that focus on changing the topology of a complex network as a form of reduction or even elimination of an infectious disease.

scholarly journals Topological Characteristics and Vulnerability Analysis of Rural Traffic Network

2019 ◽  
Vol 2019 ◽  
pp. 1-9
Author(s):  
Xia Zhu ◽  
Weidong Song ◽  
Lin Gao
Keyword(s):  
Complex Network ◽  
Network Model ◽  
Network Models ◽  
Mapping Method ◽  
Traffic Network ◽  
Complex Network Theory ◽  
The Road ◽  
Topological Features ◽  
Attack Patterns ◽  
Road Mapping

Rural traffic network (RTN), as a complex network, plays a significant role in the field of resisting natural disasters and emergencies. In this paper, we analyze the vulnerability of RTN via three traffic network models (i.e., No-power Traffic Network Model (NTNM), Distance Weight Traffic Network Model (DWTNM), and Road Level Weight Traffic Network Model (RLWTNM)). Firstly, based on the complex network theory, RTN is constructed by using road mapping method, according to the topological features. Secondly, Random Attack (RA) and Deliberate Attack (DA) strategies are used to analyze network vulnerability in three rural traffic network models. By analyzing the attack tolerance of RTN under the condition of different attack patterns, we find that the road level weight traffic network has a good performance to represent the vulnerability of RTN.

Complex Network Theory in the Application of Optimization Topology Network

2014 ◽  
Vol 651-653 ◽  
pp. 1811-1815 ◽  
Author(s):  
Bao Kun An ◽  
Yan Feng
Keyword(s):  
Complex Network ◽  
Network Topology ◽  
Network Theory ◽  
Computer Network ◽  
Rapid Development ◽  
Network Models ◽  
Complex Network Theory ◽  
New Development ◽  
The Times ◽  
Complex Network Models

Complex network theory is a new theory, which is rising with the rapid development of the computer. At present, the network structure of computers more complex network models already existed, has been unable to meet its topological properties. So, emerge as the times require complex network theory, provides a new development ideas and platform at the same time, complex network theory to the study of computer network topology. This paper firstly introduces the complex network theory, and then about the application of complex network theory in computer topology behavior, research on computer network topology and the experiment and model specific about complex network theory. More reduction.

A Literature Review Study of Software Defect Prediction using Machine Learning Techniques

2018 ◽  
Vol 6 (6) ◽  
pp. 300 ◽  
Author(s):  
Feidu Akmel ◽  
Ermiyas Birihanu ◽  
Bahir Siraj
Keyword(s):  
Machine Learning ◽  
Software Metrics ◽  
Quality Standard ◽  
Machine Learning Techniques ◽  
Software Systems ◽  
Health Care Insurance ◽  
Software Defect ◽  
Learning Techniques ◽  
Software Product

Software systems are any software product or applications that support business domains such as Manufacturing,Aviation, Health care, insurance and so on.Software quality is a means of measuring how software is designed and how well the software conforms to that design. Some of the variables that we are looking for software quality are Correctness, Product quality, Scalability, Completeness and Absence of bugs, However the quality standard that was used from one organization is different from other for this reason it is better to apply the software metrics to measure the quality of software. Attributes that we gathered from source code through software metrics can be an input for software defect predictor. Software defect are an error that are introduced by software developer and stakeholders. Finally, in this study we discovered the application of machine learning on software defect that we gathered from the previous research works.

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