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Research of Software Defect Prediction Model Based on Complex Network and Graph Neural Network.

Mengtian Cui1, Songlin Long1, Yue Jiang1

  • 1Key Laboratory of Computer System, State Ethnic Affairs Commission, Southwest Minzu University, Chengdu 610041, China.

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Summary
This summary is machine-generated.

This study introduces a novel graph neural network (GNN) framework for software defect prediction, improving accuracy by considering module connections. The GNN model enhances defect prediction metrics compared to traditional methods.

Keywords:
community detectioncomplex networkgraph convolutional neural networksoftware defect prediction

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Area of Science:

  • Computer Science
  • Software Engineering
  • Artificial Intelligence

Background:

  • Traditional software defect prediction models primarily analyze code features, neglecting inter-module relationships.
  • Understanding software module dependencies is crucial for accurate defect prediction.

Purpose of the Study:

  • To propose a novel software defect prediction framework using graph neural networks (GNNs) from a complex network perspective.
  • To leverage the relationships between software modules for more effective defect prediction.

Main Methods:

  • Representing software as a graph with classes as nodes and dependencies as edges.
  • Employing community detection algorithms to partition the software graph into subgraphs.
  • Utilizing an improved GNN model to learn node representation vectors for defect classification.

Main Results:

  • The proposed GNN framework demonstrated significant improvements in accuracy, F-measure, and Matthews correlation coefficient (MCC) on the PROMISE dataset.
  • Both spectral and spatial domain graph convolution methods within the GNN showed performance gains over benchmark models.
  • Average metric improvements ranged from 6.3% to 17.5% depending on the specific metric and convolution method used.

Conclusions:

  • The graph neural network approach effectively captures inter-module dependencies, leading to enhanced software defect prediction.
  • This complex network perspective offers a promising direction for advancing software defect prediction methodologies.