Hyperparameter settings.

Software defect prediction is a technology that uses known software information to predict defects in the target software. Generally, models are built using features such as software metrics, semantic information, and software networks. However, due to the complex software structure and the small number of samples, without effective feature representation and feature extraction methods, it is impossible to fully utilize software features, which can easily lead to misjudgments and reduced performance. In addition, a single feature cannot fully characterize the software structure. Therefore, this research proposes a new method to efficiently and accurately represent the Abstract Syntax Tree(AST) and a model called MFA(Multi Features Attention) that uses a deformable attention mechanism to extract features and uses a self-attention mechanism to fuse semantic and network features. By selecting 21 Java projects and comparing them with multiple models for cross-version and cross-project experiments, the experiments show that the average ACC, F1, AUC of the proposed model in the cross-version scheme reach 0.7, 0.614 and 0.711. In the cross-project scheme, the average ACC, F1 and AUC are 0.687, 0.575 and 0.696. Up to 41% better than other models, and the results of fusion features are better than those of a single feature, showing that MFA using two features for extraction and fusion has greater advantages in prediction performance.