scholarly journals Security Feature Measurement for Frequent Dynamic Execution Paths in Software System

2018 ◽  
Vol 2018 ◽  
pp. 1-10
Author(s):  
Qian Wang ◽  
Jiadong Ren ◽  
Xiaoli Yang ◽  
Yongqiang Cheng ◽  
Darryl N. Davis ◽  
...  
Keyword(s):  
Complex Network ◽  
Fault Location ◽  
Attenuation Factor ◽  
Cumulative Effect ◽  
Good Time ◽  
Software System ◽  
Software Complex ◽  
Feature Measurement ◽  
Security Feature ◽  
Dynamic Execution

The scale and complexity of software systems are constantly increasing, imposing new challenges for software fault location and daily maintenance. In this paper, the Security Feature measurement algorithm of Frequent dynamic execution Paths in Software, SFFPS, is proposed to provide a basis for improving the security and reliability of software. First, the dynamic execution of a complex software system is mapped onto a complex network model and sequence model. This, combined with the invocation and dependency relationships between function nodes, fault cumulative effect, and spread effect, can be analyzed. The function node security features of the software complex network are defined and measured according to the degree distribution and global step attenuation factor. Finally, frequent software execution paths are mined and weighted, and security metrics of the frequent paths are obtained and sorted. The experimental results show that SFFPS has good time performance and scalability, and the security features of the important paths in the software can be effectively measured. This study provides a guide for the research of defect propagation, software reliability, and software integration testing.

A Discovery Method of the Dirty Data Transmission Path Based on Complex Network

2014 ◽  
Vol 651-653 ◽  
pp. 1741-1747
Author(s):  
Xiao Lin Zhao ◽  
Gang Hao ◽  
Chang Zhen Hu ◽  
Zhi Qiang Li
Keyword(s):  
Complex Network ◽  
Data Transmission ◽  
Software System ◽  
Power Law Distribution ◽  
Scale Free ◽  
Transmission Path ◽  
Binary File ◽  
Weighted Directed Graph ◽  
Dirty Data ◽  
Discovery Method

With the increasing scale of software system, the interaction between software elements becomes more and more complex, which lead to the increased dirty data in running software system. This may reduce the system performance and cause system collapse. In this paper, we proposed a discovery method of the dirty data transmission path based on complex network. Firstly, the binary file is decompiled and the function call graph is drawn by using the source code. Then the software structure is described as a weighted directed graph based on the knowledge of complex network. In addition, the dirty data node is marked by using the power-law distribution characteristics of the scale-free network construction of complex network chart. Finally, we found the dirty data transmission path during software running process. The experimental results show the transmission path of dirty data is accurate, which confirmed the feasibility of the method.

scholarly journals DEVELOPMENT OF THE HARDWARE AND SOFTWARE COMPLEX CONTROLLING ROBOTIC DEVICES BY MEANS OF BIOELECTRIC SIGNALS OF THE BRAIN AND MUSCLES

2016 ◽  
Vol 1 (3) ◽  
pp. 77-82
Author(s):  
S Yu Gordleeva ◽  
S A Lobov ◽  
V I Mironov ◽  
I A Kastalskiy ◽  
M V Lukoyanov ◽  
...  
Keyword(s):  
Software System ◽  
Effort Estimation ◽  
Eeg Signals ◽  
Proportional Control ◽  
Software Complex ◽  
Emg Patterns ◽  
Robotic Devices ◽  
Bioelectric Signals ◽  
The Brain ◽  
Speed And Accuracy

Aim - to develop a hardware-software complex with combined command-proportional control of robotic devices based on electromyography (EMG) and electroencephalography (EEG) signals. Materials and methods. EMG and EEG signals are recorded using our original units. The system also supports a number of commercial EEG and EMG recording systems, such as NVX52 (MCS ltd, Russia), DELSYS Trigno (Delsys Inc, USA), MYO Thalmic (Thalmic Labs, Canada). Raw signals undergo preprocessing and feature extraction. Then features are fed to classifiers. The interpretation unit controls robotic devices on the base of classified EEG- and EMG-patterns and muscle effort estimation. The number of controlled devices includes mobile robot LEGO NXT Mindstorms (LEGO, Denmark), humanoid robot NAO (Aldebaran, France) and exoskeleton Ilia Muromets (UNN, Russia). Results. We have developed and tested an interface combining command and proportional control based on EMG signals. We have determined the parameters providing optimal characteristics of classification accuracy of EMG patterns, as well as the speed and accuracy of proportional control. Also we have developed and tested a BCI interface based on motor imagined patterns. Both EMG and EEG interfaces are included into hardware and software system. The system combines outputs of the interfaces and sends commands to a robotic device. Conclusion. We have developed and approved the hardware-software system on the basis of the combined command-proportional EMG and EEG control of external robotic devices.

An Empirical Study on the Network Model and the Online Knowledge Production Structure

2022 ◽  
pp. 599-611
Author(s):  
Quan Chen ◽  
Jiangtao Wang ◽  
Ruiqiu Ou ◽  
Sang-Bing Tsai
Keyword(s):  
Complex Network ◽  
Network Model ◽  
Knowledge Production ◽  
Mass Production ◽  
Network Size ◽  
Software System ◽  
Production Structure ◽  
R Software ◽  
Knowledge Propagation ◽  
New Perspective

Mass production has attracted much attention as a new approach to knowledge production. The R software system is a typical product of mass production. For its unique architecture, the R software system accurately recorded the natural process of knowledge propagation and inheritance. Thus, this article established a dynamic complex network model based on the derivative relationship between R software packages, which reflects the evolution process of online knowledge production structure in R software system, and studied the process of knowledge propagation and inheritance via the dynamic complex network analysis method. These results show that the network size increases with time, reflecting the tendency of R software to accelerate the accumulation of knowledge. The network density and network cohesion decrease with the increase of scale, indicating that the knowledge structure of R software presents a trend of expansion. The unique extension structure of R software provides a rich research foundation for the propagation of knowledge; thus, the results can provide us a new perspective for knowledge discovery and technological innovation.

An algorithm to find critical execution paths of software based on complex network

2015 ◽  
Vol 26 (09) ◽  
pp. 1550101 ◽  
Author(s):  
Guoyan Huang ◽  
Bing Zhang ◽  
Rong Ren ◽  
Jiadong Ren
Keyword(s):  
Complex Network ◽  
Efficient Method ◽  
Software Reliability ◽  
Path Generation ◽  
Similarity Matrix ◽  
Software Complex ◽  
Sources And Sinks ◽  
Execution Path ◽  
Software Execution ◽  
Similarity Matrices

The critical execution paths play an important role in software system in terms of reducing the numbers of test date, detecting the vulnerabilities of software structure and analyzing software reliability. However, there are no efficient methods to discover them so far. Thus in this paper, a complex network-based software algorithm is put forward to find critical execution paths (FCEP) in software execution network. First, by analyzing the number of sources and sinks in FCEP, software execution network is divided into AOE subgraphs, and meanwhile, a Software Execution Network Serialization (SENS) approach is designed to generate execution path set in each AOE subgraph, which not only reduces ring structure's influence on path generation, but also guarantees the nodes' integrity in network. Second, according to a novel path similarity metric, similarity matrix is created to calculate the similarity among sets of path sequences. Third, an efficient method is taken to cluster paths through similarity matrices, and the maximum-length path in each cluster is extracted as the critical execution path. At last, a set of critical execution paths is derived. The experimental results show that the FCEP algorithm is efficient in mining critical execution path under software complex network.

scholarly journals An Empirical Study on the Network Model and the Online Knowledge Production Structure

2019 ◽  
Vol 12 (4) ◽  
pp. 171-182
Author(s):  
Quan Chen ◽  
Jiangtao Wang ◽  
Ruiqiu Ou ◽  
Sang-Bing Tsai
Keyword(s):  
Complex Network ◽  
Network Model ◽  
Knowledge Production ◽  
Mass Production ◽  
Network Size ◽  
Software System ◽  
Production Structure ◽  
R Software ◽  
Knowledge Propagation ◽  
New Perspective

Mass production has attracted much attention as a new approach to knowledge production. The R software system is a typical product of mass production. For its unique architecture, the R software system accurately recorded the natural process of knowledge propagation and inheritance. Thus, this article established a dynamic complex network model based on the derivative relationship between R software packages, which reflects the evolution process of online knowledge production structure in R software system, and studied the process of knowledge propagation and inheritance via the dynamic complex network analysis method. These results show that the network size increases with time, reflecting the tendency of R software to accelerate the accumulation of knowledge. The network density and network cohesion decrease with the increase of scale, indicating that the knowledge structure of R software presents a trend of expansion. The unique extension structure of R software provides a rich research foundation for the propagation of knowledge; thus, the results can provide us a new perspective for knowledge discovery and technological innovation.

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