Research On Characteristic Analysis Method Of Software Group Based On Software Network And Interactive Pattern

With the development and popularization of Internet technology,software has also developed from traditional single-machine software to multi-machine software with network communication function.At the same time,large-scale software system has also developed into multiple software individuals that can run independently and have interactive features.In the Internet mode,the interaction between software becomes more and more frequent.Software with interactive characteristics gradually forms a group,and its individual behavior continuously affects the characteristics of the group.At present,the software system is widely used in all walks of life and has become an important part of the development of the industry.How to ensure the software system to run stably and efficiently has become an important research topic.The stability of software group is important cornerstones to ensure economic efficient development.Analysis of software group characteristics,identification of key features,and then group stability measurement become an important means to maintain software group.From the view of software network and software interactive pattern,the characteristic analysis methods of key nodes,key patterns and stability in software group are studied.The main content of this paper is as follows.Firstly,a complex software group network model based on time series and interactive weight is constructed by using the feature of mutual calls among software in the software group.The interactive characteristics of software group,especially the dependence of class libraries,data exchange,mutual invocation and data sharing among software group,are studied,and software group is defined.According to the characteristics of time series of software invocation,software interactive weight is proposed to measure the importance of interaction.In order to improve the maintenance efficiency of software group and obtain more interested key nodes in software group network,an algorithm SG-CPMining is proposed to discover key nodes in the network community of software group.The basic process is to first acquire software communities in the software group network.A community discovery algorithm SG-GroupMing is proposed to obtain influential child network in software group network.At the same time,two pruning strategies,community support and edge betweenness threshold,are designed to obtain communities with higher interest.In order to find influential software nodes in the software community,a key node discovery algorithm SG-CPMining based on node degree is proposed.Secondly,to obtain software and software group with important interactions in software group,this paper proposes a key pattern mining algorithm SG-FIP based on interactive sequences in a software group.A sequential interactive sequence model is established by using the information flow of data exchange,data sharing and mutual calling among software.On the basis of the software interactive model,a sequence weight based on a time period and interaction quantity is defined to enhance the interest of interactive patterns.A key pattern mining algorithm SG-FIP based on interactive sequences in software group is put forward to get key software and software groups with frequent interactive characteristic.In order to obtain a higher interest and leaner mining results,the pruning factor ISPe is designed.The SG-FIP algorithm is executed within a predefined sliding time period to obtain a more targeted and real-time key interactive sequence pattern.Thirdly,in view of the fact that software interaction has the characteristics of changing importance with time interval and time lapse,a key pattern mining algorithm called KPM-SENStream based on time-interval weight and pattern decay in the network stream of software interaction is put forward in this paper.The interactive sequence stream of software is clearly defined as continuous and the infinite number of interactive information flows between software with time stamps.The key patterns of software interaction are mined with the time window sliding.The mining model of pattern decay is built based on the continuous sliding windows.Considering the different importance with various time-intervals,the time-interval weight is designed.The KPM-SENStream algorithm is adopted to obtain key interactive patterns in the dynamic software group network.Finally,in order to improve mining efficiency of interactive patterns under the condition of large amount of interactive data,a distributed frequent interactive pattern mining algorithm HFIS-Mine is proposed.The software group interactive items,weighted interactive sequences,frequent interactive sequences,key nodes,and the destructive power of group stability are defined.Hadoop and Spark framework are introduced.Based on the definition of interactive pattern and weighted interactive sequence,key nodes and key interactive items are mined.Based on HFIS-Mine algorithm,an algorithm SG-StaMea is proposed to measure group stability based on the loss of connectivity caused by removing key nodes and key edges from the group network.The feasibility of each algorithm is verified through experiments,and it is compared with the existing algorithms.