Research on Software Network Key Nodes Mining Methods Based on Complex Network

2020 ◽  
pp. 193-205
Author(s):  
Chun Shan ◽  
Peng Wang ◽  
Changzhen Hu ◽  
Xianwei Gao ◽  
Shanshan Mei
Keyword(s):  
Complex Network ◽  
Software Network ◽  
Mining Methods ◽  
Key Nodes

Testing Techniques of the Software Network Interface Based on the Capture and Analysis of the Packet

2012 ◽  
Vol 263-266 ◽  
pp. 1786-1791
Author(s):  
Song Yang Du ◽  
Jia Si Wang ◽  
Zhong Wei Chen ◽  
Di Ming Ai
Keyword(s):  
Complex Network ◽  
Network Interface ◽  
Software Network ◽  
Testing Techniques ◽  
Interface Protocol

Nowadays utilized through the network, the majority of the software has their own particular and complex network interface transmission protocols. Which makes the validation of the interface protocols becomes significant and difficult in the interface testing of the software. In this paper, the network interface protocol is analyzed to help validate the software network interface, based on the capture, analysis, construction and the transmission of the packet. By using this method, the software network interface will be more effectively tested.

Analysis on Key Nodes Behavior for Complex Software Network

2012 ◽  
pp. 59-66 ◽  
Author(s):  
Xizhe Zhang ◽  
Guolong Zhao ◽  
Tianyang Lv ◽  
Ying Yin ◽  
Bin Zhang
Keyword(s):  
Software Network ◽  
Key Nodes

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.

Mining Algorithm for the Core Node in the Complex Network of E-Commerce

2014 ◽  
Vol 556-562 ◽  
pp. 4577-4581
Author(s):  
Bing Xiao
Keyword(s):  
Information Asymmetry ◽  
Complex Network ◽  
Adverse Selection ◽  
Moral Hazards ◽  
The Core ◽  
Core Node ◽  
Mining Algorithm ◽  
Selection For ◽  
Risk Protection ◽  
Key Nodes

Through digging out the core suppliers and core customers from the numerous suppliers and customers in the complex network of E-commerce, it contributes to reducing the adverse selection for the consumers and moral hazards for the operators caused by information asymmetry. Meanwhile, it is very meaningful for the credit risk protection in the complex network of E-commerce. On the basis of the references to the White and Smyth algorithms, in this paper, improvements from the White and Smyth algorithms are made herein, combining several features of the E-commerce complex network such as competitiveness, incomplete information and unsymmetrical information. In addition, an algorithm for mining the key nodes in E-commerce complex network is put forward, and applications are explained by instances.

Distributed Frequent Interactive Pattern-Based Complex Software Group Network Stability Measurement

2018 ◽  
Vol 28 (05) ◽  
pp. 619-641
Author(s):  
Weina Li ◽  
Jiadong Ren
Keyword(s):  
Frequent Pattern ◽  
Interactive Software ◽  
Network Stability ◽  
Group Stability ◽  
Software Network ◽  
Stability Measurement ◽  
The Stability ◽  
Runtime Environments ◽  
Software Group ◽  
Key Nodes

Interactive software can run not only independently but also often collaboratively to perform tasks thus forming a larger group of software networks. Hence the analysis of interactions is essential as a way to measure the stability of the entire software group network, i.e. the interactive patterns and frequency. However, current studies rarely investigate the performance of software as groups but as individuals thus omitting their interactions. Especially, the performance of some traditional measurement algorithms which execute in nondistributed runtime environments is poor. In this paper, we proposed a new software group stability model concentrating on software network level behaviors as a group. An algorithm is proposed to extract key nodes and critical interactive items based on frequent interaction pattern, then the stability of software group can be assessed based on the loss of connectivity caused by removing key nodes and key edges from the network, using the algorithm SG-StaMea. Furthermore, our algorithms can quantify the stability. To validate the efficacy of our model, the Spark and Hadoop platforms have been selected as targets systems. Both experiments and experimental data showed that our algorithms have significantly improved the accuracy of software stability measurement compared to classical algorithm such as Apriori of frequent pattern.

scholarly journals Identification of Key Nodes in Aircraft State Network Based on Complex Network Theory

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 60957-60967 ◽  
Author(s):  
Wang Zekun ◽  
Wen Xiangxi ◽  
Wu Minggong
Keyword(s):  
Complex Network ◽  
Network Theory ◽  
Complex Network Theory ◽  
Key Nodes

scholarly journals Cooperative Multi-UAV Collision Avoidance Based on a Complex Network

2019 ◽  
Vol 9 (19) ◽  
pp. 3943 ◽  
Author(s):  
Huang ◽  
Tang ◽  
Lao
Keyword(s):  
Conflict Resolution ◽  
Collision Avoidance ◽  
Complex Network ◽  
Computing Time ◽  
Real Life ◽  
Traffic Density ◽  
Contraction Method ◽  
Cn Algorithm ◽  
Multi Uav ◽  
Key Nodes

The conflict resolution problem in cooperative unmanned aerial vehicle (UAV) clusters sharing a three-dimensional airspace with increasing air traffic density is very important. This paper innovatively solves this problem by employing the complex network (CN) algorithm. The proposed approach allows a UAV to perform only one maneuver—that of the flight level change. The novel UAV conflict resolution is divided into two steps, corresponding to the key node selection (KS) algorithm based on the node contraction method and the sense selection (SS) algorithm based on an objective function. The efficiency of the cooperative multi-UAV collision avoidance (CA) system improved a lot due to the simple two-step collision avoidance logic. The paper compares the difference between random selection and the use of the node contraction method to select key nodes. Experiments showed that using the node contraction method to select key nodes can make the collision avoidance effect of UAVs better. The CA maneuver was validated with quantitative simulation experiments, demonstrating advantages such as minimal cost when considering the robustness of the global traffic situation, as well as significant real-time and high efficiency. The CN algorithm requires a relatively small computing time that renders the approach highly suitable for solving real-life operational situations.

scholarly journals Mining the Key Nodes from Software Network Based on Fault Accumulation and Propagation

2019 ◽  
Vol 2019 ◽  
pp. 1-11
Author(s):  
Huang Guoyan ◽  
Wang Qian ◽  
Liu Xinqian ◽  
Hao Xiaobing ◽  
Yan Huaizhi
Keyword(s):  
Network Model ◽  
Propagation Characteristic ◽  
Fault Propagation ◽  
Fault Probability ◽  
Software Complexity ◽  
Probability Metric ◽  
Software Network ◽  
Fault Severity ◽  
Accumulation Characteristic ◽  
Key Nodes

The increasement of software complexity directly results in the augment of software fault and costs a lot in the process of software development and maintenance. The complex network model is used to study the accumulation and accumulation of faults in complex software as a whole. Then key nodes with high fault probability and powerful fault propagation capability can be found, and the faults can be discovered as soon as possible and the severity of the damage to the system can be reduced effectively. In this paper, the algorithm MFS_AN (mining fault severity of all nodes) is proposed to mine the key nodes from software network. A weighted software network model is built by using functions as nodes, call relationships as edges, and call times as weight. Exploiting recursive method, a fault probability metric FP of a function, is defined according to the fault accumulation characteristic, and a fault propagation capability metric FPC of a function is proposed according to the fault propagation characteristic. Based on the FP and FPC, the fault severity metric FS is put forward to obtain the function nodes with larger fault severity in software network. Experimental results on two real software networks show that the algorithm MFS_AN can discover the key function nodes correctly and effectively.

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