Defect Prediction of Finite State Machine Models Based on Transfer Learning

As software systems become increasingly intricate, predicting cache defects has emerged as a crucial aspect of maintaining software quality. This article introduces a novel approach for predicting cache defects, utilizing a transfer learning (TL) software deterministic finite state machine (DFSM) model. Finite State Machine (DFSM) model defect prediction based on transfer learning is an innovative software defect prediction method. This method combines the advantages of transfer learning (TL) and deterministic finite state machine (DFSM). Intended to improve the effectiveness and accuracy of software cache defect prediction. This innovative method seeks to enhance the effectiveness of predicting cache issues within software. By merging the precision of DFSM with TL's versatility, the proposed technique is transferable to target projects through training and learning from source projects, addressing data scarcity challenges in new or evolving projects. This method utilizes transfer learning (TL) strategy to transfer knowledge from the source project to the target project through learning and training, thereby solving the problem of data scarcity. Experimental findings reveal that as training data grows, the method's test coverage and fault detection rate steadily increase. Additionally, it demonstrates impressive execution efficiency and stability. In comparison to traditional methods, this approach exhibits substantial benefits in elevating software quality and reliability, offering a fresh and efficient tool for ensuring software quality. Thanks to the TL strategy, the method rapidly adapts to the unique environments and requirements of new or evolving projects, thereby enhancing forecasting accuracy and efficiency.

@article{Zhang2024,
  title     = {Defect Prediction of Finite State Machine Models Based on Transfer Learning},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {15},
  number    = {6},
  year      = {2024},
  publisher = {The Science and Information Organization},
  author    = {Wei Zhang},
  doi       = {10.14569/IJACSA.2024.0150689},
  url       = {https://doi.org/10.14569/IJACSA.2024.0150689}
}