A Quantitative Analysis Framework for Recurrent Neural Network Recurrent neural network (RNN) has achieved great success in processing sequential inputs for applications such as automatic speech recognition, natural language processing and machine translation. However, quality and reliability issues of RNNs make them vulnerable to adversarial attacks and hinder their deployment in realworld applications. In this paper, we propose a quantitative analysis framework  DeepStellar  to pave the way for effective quality and security analysis of software systems powered by RNNs. DeepStellar is generic to handle various RNN architectures, including LSTM and GRU, scalable to work on industrialgrade RNN models, and extensible to develop customized analyzers and tools. We demonstrated that, with DeepStellar, users are able to design efficient test generation tools, and develop effective adversarial sample detectors. We tested the developed applications on three real RNN models, including speech recognition and image classification. DeepStellar outperforms existing approaches three hundred times in generating defecttriggering tests and achieves 97% accuracy in detecting adversarial attacks. A video demonstration which shows the main features of DeepStellar is available at: https:sites.google.comviewdeepstellartooldemo.

Project Achilles: A Prototype Tool for Static MethodLevel Vulnerability Detection of Java Source Code Using a Recurrent Neural Network Software has become an essential component of modern life, but when software vulnerabilities threaten the security of users, new ways of analyzing for software security must be explored. Using the National Institute of Standards and Technologyu0027s Juliet Java Suite, containing thousands of examples of defective Java methods for a variety of vulnerabilities, a prototype tool was developed implementing an array of LongShort Term Memory Recurrent Neural Networks to detect vulnerabilities within source code. The tool employs various data preparation methods to be independent of coding style and to automate the process of extracting methods, labeling data, and partitioning the dataset. The result is a prototype commandline utility that generates an ndimensional vulnerability prediction vector. The experimental evaluation using 44,495 test cases indicates that the tool can achieve an accuracy higher than 90% for 24 out of 29 different types of CWE vulnerabilities.