Parallel multi-objective evolutionary optimization based dynamic community detection in software ecosystem

Building a dynamic network in a software ecosystem and detecting its communities can not only observe the structure of the dynamic network, but also reveal the evolution of these communities. However, previous methods cannot timely and accurately detect its communities. In view of this, we propose a method of dynamic community detection based on parallel multi-objective evolutionary optimization in this paper. In the proposed method, a dynamic network in a software ecosystem is first built based on the relationship between entities. The relationship is often time-dependent. Then, changed/unchanged connected components of the current network and time-dependent/independent sub-networks are obtained by recognizing the change of this network. Further, previous communities of each unchanged connected component remain unchanged, whereas ones of each time-dependent sub-network are detected based on parallel multi-objective evolutionary optimization. In this way, communities of each changed connected component are obtained based on ones of the time-dependent sub-network and previous ones of the time-independent sub-network, and ones of the current network after the change are formed. Five dynamic networks in a software ecosystem are built using data crawled in GitHub. Based on them, a series of experimental results demonstrate that the proposed method is advantageous.