Abstract: With the increasing complexity of software,the scale and complexity of program defects are also increasing.Program defects not only consume a large amount of development costs but also lead to real-world security issues.Existing program repair methods generally suffer from poor repair effectiveness and high training costs.To address these issues,this paper proposes an automatic program repair method based on perturbation and freezing of pre-trained models.By adding noise to the model parameters through a matrix-based perturbation method,it alleviates the overfitting problem of pre-trained models on the program repair task during fine-tuning.Furthermore,freezing the encoder in the pre-trained model reduces the model’s training time and computational resource consumption.Additionally,the checkpoint ensemble strategy is adopted to enhance the model’s repair effectiveness.Experiments on 40 Python programs in the QuixBugs dataset demonstrate that the proposed method has significant advantages in reducing model training time and computational resource consumption,as well as in repair effectiveness.It only requires training 41.62% of the parameters of the original model,reduces training time by 39.16%,and can repair 70% of the defects in the dataset,demonstrating the diversity of the repaired defect types.

Key words: Automated program repair, Deep learning, Pre-trained model, Fine-tuning, Checkpoint ensemble