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

Abstract

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.