Identification of Key Classes in Software Systems Based on Graph Neural Networks

Abstract

There are usually some key classes which are in the core position in the topology structure of software systems.The defects in these classes will bring great security risks to the system.Therefore,it is very important to identify these key classes for engineers to understand or maintain an unfamiliar software system.To do this,the paper proposes a novel method of identifying key classes based on graph neural networks.Specifically,the software system is abstracted as software network by using complex network theory,and then by combining unsupervised network embedding learning and neighborhood aggregation mode,we construct an encoder-decoder framework to extract the representation vector of class nodes in software system.Finally,according to the obtained node representations,Pairwise learning-to-rank algorithm is adopted to realize the importance ranking of nodes,so as to achieve the identification of key classes in software system.In order to verify the effectiveness of our method,an empirical analysis of four object-oriented Java open-source software is done,and we compare it with five commonly used node importance measurement methods and two existing works.The experimental results show that,compared with node centrality,K-core and PageRank,the proposed method is more effective in identifying key classes from the perspective of network robustness.In addition,on the existing public labeled dataset,the recall and precision of this paper are better at the top 15% percent of nodes,and improved by more than 10%.

Keywords

• software network|key class identification|network embedding|graph neural network|learning to rank