KEYWORDS: Operating systems, Failure analysis, Design, Error analysis, Analytical research, Software development, Databases, Control systems, Associative arrays, FDA class I medical device development
Aiming at the problems of frequent occurrence of domestic operating system robustness problems, insufficient test coverage and low defect detection rate at this stage, we carry out the research on operating system robustness testing and propose a method of constructing operating system robustness test cases based on FMEA (Failure Mode and Effect Analysis) technology, which applies the FMEA technology to software robustness testing. In this paper, we design FMEA analysis guidelines using the requirements of relevant standards in the testing field, and guide testers to analyze the potential failure modes in the relevant documents of the operating system according to the guidelines, and carry out the failure mode and impact analysis to further construct the set of robustness test cases. Finally, through experimental verification, this method gives full play to the advantages of FMEA in the field of robustness testing, and the number of defects found has been greatly improved, which effectively improves the quality of the robustness test cases and further improves the robustness of the domestic operating system, and it has strong engineering application value.
Due to the excessive cost of data collection as well as annotation in dedicated domains, artificial intelligence model training is difficult to achieve optimality with insufficient data. To optimize this issue, a text generation data augmentation method based on the BERT model is proposed in this paper for augmenting the small amount of available annotated data. The optimization of this data augmentation method is demonstrated by experiments. In a text classification experiment, this data augmentation method can improve the training effect of the source data by 2.9%.
Aiming at the problems of high target similarity and strong camouflage in the domain-specific, and the traditional image classification technology is difficult to achieve accurate classification, it is proposed to use the deep neural network Resnet50 as the feature extraction network, and combine the attention mechanism and improve it, which can improve the ability of learning effective features; and use depthwise separable convolution to replace standard convolution, which can reduce computational parameters and save computational space. It is verified by experiments that the accuracy of the improved algorithm in this paper is 0.71% higher than that of the Resnet50 prototype, and 0.39 % higher than that of the Resnet50 +SE algorithm model in the image classification of the domain-specific.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.