A method for identifying key classes in software systems based on graph neural network

Abstract

The invention discloses a method for identifying key classes in a software system based on a graph neural network, including: (1) using open source tools to analyze software source code files, extracting various associations between classes, and constructing class classes according to these relations; The weighted software network model between them; (2) Use the network embedding method to learn the nodes in the weighted software network to obtain the embedding vector of the node; (3) Build a ranking model based on graph neural network (4) Use pairwise sorting loss Train the model to rank the importance of nodes in the network. The invention has the advantages of: under the condition of ensuring the identification accuracy, it can analyze the large-scale software system within an extremely low time complexity, and identify potential key classes therefrom. It can help developers understand the software structure and improve the stability of the software system.

Description

technical field [0001] The invention relates to the technical field of software engineering software network analysis, in particular to a method for identifying key classes in a software system based on a graph neural network. Background technique [0002] As the scale of the network continues to expand, the complexity of the software system continues to increase, and defects in the software are inevitable. It is becoming more and more difficult to find these defects. Once these defects are exposed during the use of software, it often affects not only the module itself where the defect is located, but the entire software system may collapse. Some studies have pointed out that defects in software systems are similar to the propagation model of diseases, and will spread to other modules without defects along with the dependencies between various modules in the software system, such as method calls and parameter transfers, resulting in no defects. The module of the last also h...

AI Technical Summary

Problems solved by technology

It is becoming more and more difficult to find these defects. Once these defects are exposed in the process of using the software, it often affects not only the module itself where the defect is located, but the entire software system may collapse.

When the above methods calculate the importance of nodes, most of them are based on the local information of nodes, and some methods based on global information have high computational complexity in real large-scale networks.

In addition, the above methods are all carried out on the basis of manually constructing metrics, and the graph neural network has not been used to automatically learn the global structural information of the network and apply the learning results to the key class identification research of software systems.

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