Interaction Prediction of Multi-granularity Software System Based on Graph Neural Network
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    Abstract:

    The interactions between elements in contemporary software systems are notably intricate, encompassing relationships between packages, classes, and functions. Accurate comprehension of these relationships is pivotal for optimizing system structures and enhancing software quality. Analyzing inter-package relationships can help unveil dependencies between modules, thereby assisting developers in more effectively managing and organizing software architectures. On the other hand, a clear understanding of inter-class relationships contributes to the creation of code repositories that are more scalable and maintainable. Moreover, a clear understanding of inter-function relationships facilitates rapid identification and resolution of logical errors within programs, consequently enhancing the robustness and reliability of the software. However, current predictions of software system interaction confront challenges such as granularity disparities, inadequate features, and version changes. To address this challenge, this study constructs corresponding software network models based on the three granularities, including software packages, classes, and functions. It introduces a novel approach combining local and global features to reinforce the analysis and prediction of software systems through feature extraction and link prediction of software networks. This approach is based on the construction and handling of software networks, involving specific steps such as leveraging the node2vec method to learn local features of software networks and combining Laplacian feature vector encoding to comprehensively represent the global positional information of nodes. Subsequently, the Graph Transformer model is employed to further optimize the feature vectors of node attributes, culminating in the completion of the interaction prediction task of the software system. Extensive experimental validations are conducted on three Java open-source projects, encompassing within-version and cross-version interaction prediction tasks. The experimental results demonstrate that, compared to benchmark methods, the proposed approach achieves an average increase of 8.2% and 8.5% in AUC and AP values, respectively in within-version prediction tasks. This approach reaches an average rise of 3.5% and 2.4% in AUC and AP values, respectively, in cross-version prediction tasks.

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邓文涛,程璨,何鹏,陈孟瑶,李兵.基于图神经网络的多粒度软件系统交互关系预测.软件学报,,():1-21

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History
  • Received:December 27,2023
  • Revised:February 01,2024
  • Adopted:
  • Online: June 20,2024
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