Survey on identification and prediction of security threats using various deep learning models on software testing.

In this research, authors give a literature analysis of the methods used to detect and anticipate security risks in software testing by using a number of deep learning models. The purpose of this study is to conduct a literature review on the use of deep learning models in software testing for the purpose of detecting and predicting security issues. Moreover, the motive of this work is to analyze the exiting techniques related as software testing model and its application oriented performance. A thorough search of the available literature across several databases and resources forms the basis of this evaluation. There are a total of 69 publications found via the search, 34 of which are original research. Convolutional neural networks, long short-term memory networks, generative adversarial networks, and capsule networks are only some of the state-of-the-art methods for identifying and predicting security risks that are the topic of this research study. Datasets, performance indicators, and assessment criteria are all dissected for this examination. Finally, the benefits and drawbacks of using deep learning models to detect and anticipate software security flaws during testing are outlined in this article. Software testing experts and academics will benefit from this study since it will shed light on the present state of the art in the area and provide direction for the creation of innovative approaches to the detection and forecasting of security risks in software testing. [ABSTRACT FROM AUTHOR]