A Software Defect Prediction Method based on Multi-type Features and Feature Selection

Abstract

Numerous software defect prediction methods utilize semantic information and software metrics as code features, neglecting the structural knowledge inherent in the source code.Other studies improve feature completeness by simply combining different types of defect indicators, which causes information redundancy.To address these challenges, this paper proposes a novel software defect prediction method that incorporates multitype features and performs feature selection.Firstly, semantic and structural features are extracted by Text Convolutional Neural Network (TextCNN) and Graph Isomorphism Network (GIN) from Abstract Syntax Tree (AST) and Program Dependency Graph (PDG), respectively, which are combined with software metrics to build a multi-type feature set.Then, Recursive Feature Elimination with Cross-Validation (RFECV) integrating a novel feature importance measure is utilized to remove redundant features and generate a feature subset.Finally, a prediction model for classification is established based on the feature subset.The experiments validated the effectiveness of multi-type features and the improved RFECV.Overall our proposed method outperforms state-of-the-art techniques on nine Java open-source projects.