Microsoft Technology Licensing, LLC

Matthew Glenn Jin

Neelakantan Sundaresan

Xiaoyu Liu

Xin Shi

Roshanak Zilouchian Moghaddam

Shao Kun Deng

Shuvendu K Lahiri

Colin Bruce Clement

Jason Shaver

Gearard Boland

Anant Girish Kharkar

Payal Bajaj

Anshul Gupta

Eduardo Enrique Noriega De Armas

A false positive vulnerability system detects whether a software vulnerability identified by a static code vulnerability analyzer is a true vulnerability or a false positive. The system utilizes deep learning models to predict whether an identified vulnerability is accurate given the source code context of the identified vulnerability. A neural encoder transformer model is trained to classify a false positive given the method body including the identified vulnerability. A neural decoder transformer model is trained to predict a candidate line-of-code to complete a prompt inserted into the context of the identified vulnerability. The candidate line-of-code that successfully completes the prompt is used as a signal to identify that the identified vulnerability is a false positive.

False positive vulnerability detection using neural transformers

Language interoperability between source code programs not compatible with an interprocedural static code analyzer is achieved through language-independent representations of the programs. The source code programs are transformed into respective intermediate language instructions from which a language-independent control flow graph and a language-independent type environment is created. A program compatible with the interprocedural static code analyzer is generated from the language-independent control flow graph and the language-independent type environment in order to utilize the interprocedural static code analyzer to detect memory safety faults.

Language interoperability to automate code analysis

An interactive question and answer (Q&A) service provides pairs of questions and corresponding answers related to the content of a web page. The service includes pre-configured Q&A pairs derived from a deep learning framework that includes a series of neural networks trained through joint and transfer learning to generate questions for a given text passage. In addition, pre-configured Q&A pairs are generated from historical web access patterns and sources related to the content of the web page.

Question and answer pair generation using machine learning

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Matthew Glenn Jin