Microsoft Technology Licensing, LLC

Jordan Joseph Henkel

Brian Paul Kroth

Vu Minh Le

Fang Liu

Vivek Gupta

Avrilia Floratou

Irene Rogan Shaffer

Joyce Yu Cahoon

Ehimwenma Nosakhare

Jason Wang

Wee Hyong Tok

Richard Anarfi

Shaily Jignesh Fozdar

Yan Qiu

Daniel Muñoz Huerta

Abhishek Narain

Gust Verbruggen

Noelle Yanhui Li

David Joseph Donahue

A code notebook and backend cloud service are configured to intelligently analyze program source code that a developer wants analyzed. A user drafts a code query to be answered about the source code that may specify specific variables, code structure elements, and/or program flows to be scrutinized. A cloud-computing environment builds a code database of the source code and analyzes its text, code structures, and program flows-using. The code database is embedded with indications of semantic equivalence for text in the source code, identifications of different code structural elements, and program flows. In the cloud-computing environment, a query service takes the code query of the developer and queries the database with machine-learned embeddings, generating query results that are shared with the developer and shown in a representation of the source code.

Notebook for navigating code using machine learning and flow analysis

A computer-implemented method for performing natural language-based data integration includes causing execution of a data integration application on a remote device via a network and causing surfacing of a GUI corresponding to the data integration application on a display of the remote device. The method includes receiving, via the GUI, a natural language input representing a data integration task, generating, via an LLM, a set of ordered activities corresponding to the data integration task represented by the natural language input, and selecting, via the LLM, one or more APIs for performing each activity within the set of ordered activities. The method also includes generating a data pipeline based on the set of ordered activities and the API(s) for performing each activity, as well as back-translating the data pipeline to a desired data format for execution by the data integration application.

Natural language-based data integration

A code notebook and backend cloud service are configured to intelligently analyze program source code that a developer wants analyzed. A user drafts a code query to be answered about the source code that may specify specific variables, code structure elements, and/or program flows to be scrutinized. A cloud-computing environment builds a code database of the source code and analyzes its text, code structures, and program flows. The code database is embedded with indications of semantic equivalences for text in the source code, identifications of different code structural elements, and program flows. In the cloud-computing environment, a query service takes the code query of the developer and queries the database with machine-learned embeddings, generating query results that are shared with the developer and shown in a representation of the source code.

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Jordan Joseph Henkel