Code Similarity Prediction Model for Industrial Management Features Based on Graph Neural Networks

Author:

Li Zhenhao1,Lei Hang1,Ma Zhichao1,Zhang Fengyun1

Affiliation:

1. School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China

Abstract

The code of industrial management software typically features few system API calls and a high number of customized variables and structures. This makes the similarity of such codes difficult to compute using text features or traditional neural network methods. In this paper, we propose an FSPS-GNN model, which is based on graph neural networks (GNNs), to address this problem. The model categorizes code features into two types, outer graph and inner graph, and conducts training and prediction with four stages—feature embedding, feature enhancement, feature fusion, and similarity prediction. Moreover, differently structured GNNs were used in the embedding and enhancement stages, respectively, to increase the interaction of code features. Experiments with code from three open-source projects demonstrate that the model achieves an average precision of 87.57% and an F0.5 Score of 89.12%. Compared to existing similarity-computation models based on GNNs, this model exhibits a Mean Squared Error (MSE) that is approximately 0.0041 to 0.0266 lower and an F0.5 Score that is 3.3259% to 6.4392% higher. It broadens the application scope of GNNs and offers additional insights for the study of code-similarity issues.

Funder

Sichuan Provincial Science and Technology Program Funded Projects

Department of Science and Technology of Sichuan Province, China

Publisher

MDPI AG

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