Software Defect Prediction via Generative Adversarial Networks and Pre-Trained Model

Software defect prediction, which aims to predict defective modules during software development, has been implemented to assist developers in identifying defects and ensure software quality. Traditional defect prediction methods utilize manually designed features such as “Lines Of Code” that fail to capture the syntactic and semantic structures of code. Moreover, the high cost and difficulty of building the training set lead to insufficient data, which poses a significant challenge for training deep learning models, particularly for new projects. To overcome the practical challenge of data limitation and improve predictive capacity, this paper presents DP-GANPT, a novel defect prediction model that integrates generative adversarial networks and state-of-the-art code pre-trained models, employing a novel bi-modal code-prompt input representation. The proposed approach explores the use of code pre-trained model as auto-encoders and employs generative adversarial networks algorithms and semi-supervised learning techniques for optimization. To facilitate effective training and evaluation, a new software defect prediction dataset is constructed based on the existing PROMISE dataset and its associated engineering files. Extensive experiments are performed on both within-project and cross-project defect prediction tasks to evaluate the effectiveness of DP-GANPT. The results reveal that DP-GANPT outperforms all the state-of-the-art baselines, and achieves performance comparable to them with significantly less labeled data.

@article{Song2024,
  title     = {Software Defect Prediction via Generative Adversarial Networks and Pre-Trained Model},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {15},
  number    = {3},
  year      = {2024},
  publisher = {The Science and Information Organization},
  author    = {Wei Song and Lu Gan and Tie Bao},
  doi       = {10.14569/IJACSA.2024.01503119},
  url       = {https://doi.org/10.14569/IJACSA.2024.01503119}
}