# A Method for Processing Static Analysis Alarms Based on Deep Learning
## 9 Conclusions  
This study delves into the application of deep learning technology in static analysis alert processing. By combining the context embedding generated by the pre-trained model and the generalization ability of deep learning models, a large number of data samples were trained to learn the patterns in warning information and related codes. This research achieves accurate identification of actionable alarms from a large number of static analysis alarms with high false positive rates. This achievement has practical significance for developers, as they will be able to quickly respond to alarms of genuine software defects, thereby optimizing the software development process and improving software reliability and development efficiency. Firstly, this approach combines alarm information and its corresponding code fragments to construct an independent input sequence and then utilizes the powerful generalization ability of the pre-trained model to transform it into context embedding. These contextual embeddings enhance the ability of downstream classification models to extract key features from input sequences and help capture valuable contextual information from code snippets and warning information. The research results indicate that the contextual embeddings generated by pre-trained model are crucial for the performance of subsequent classification tasks, helping downstream tasks to complete more accurate classification tasks and significantly improving the accuracy of identifying actionable alarms. In addition, the experimental results of this study also indicate that the choice of neural network architecture has a significant impact on the performance of alarm classification. The experimental plan designed in this study confirmed that deep learning models, such as bidirectional recurrent neural networks and convolutional neural networks, can effectively address the challenges in the field of static analysis alarm processing. This breakthrough is expected to significantly improve the software development process, significantly enhance software reliability, and enhance the work efficiency of developers. In summary, this article proposes a static analysis alarm processing method based on pre-trained models and neural networks that was rigorously verified through empirical evaluation. We believe that this method can provide important insights for other researchers and have a profound impact on the development of static analysis alarm processing technology. Conceptualization, J.T. and Y.T. ; methodology, J.T. and Y.T. ; validation, J.T. and Y.T. ; formal analysis, J.T. and Y.T. ; investigation, J.T. and Y.T. ; resources, J.T. and Y.T. ; data curation, J.T. ; writing--original draft preparation, Y.T. ; writing--review and editing, J.T. and Y.T. ; supervision, J.T. All authors have read and agreed to the published version of the manuscript. This research was supported by the Natural Science Foundation of Hebei Province under grant F2021201049 and F2021201058. Additionally, we acknowledge the support from the Funds of the Central Government for Local Science and Technology Development under grant 236Z0701G. Any opinions, findings, or conclusions expressed in this work are those of the authors and do not reflect the views of the funding agencies in any sense. Not applicable. The dataset used to support the findings was derived from GitHub available at [[https://github.com/Don2025/defects4j](https://github.com/Don2025/defects4j)]([https://github.com/Don2025/defects4j](https://github.com/Don2025/defects4j)) (accessed on 19 April 2023). We would like to thank all the anonymous reviewers for their constructive comments on improving this paper. The authors declare no conflicts of interest. The following abbreviations are used in this manuscript: