Microsoft Technology Licensing, LLC

Shraddha Govind Barke

Sumit Gulwani

Benjamin Zorn

Andrew Donald Gordon

Mark Alistair Wilson-Thomas

Vu Minh Le

Nachiappan Nagappan

Elnaz Nouri

Advait Sarkar

Gustavo Araujo Soares

Arjun Radhakrishna

Ashish Tiwari

Carina Suzana Negreanu

Neil Toronto

Brian Paul Slininger

Sruti Srinivasa Ragavan

Xiang Gao

Christian Leopold Bejamin Poelitz

Alan Thomas Leung

The indirect querying of models to determine capabilities possessed by the model. Such indirect queries take the form of model input that potentially includes a natural language input user data. Such model input is structured such that the output of the model is either not natural language at all, or else is natural language that is not semantically responsive to the natural language input. Nevertheless, the output is evaluated to estimate or determine the capability possessed by the model. Thus, models may be more fully utilized to their better potential.

Model capability extraction

A synthesis procedure learns program transformations for a text document, on-the-fly during an edit session, from examples of concrete edits made during the edit session and from an unsupervised set of additional inputs. The additional inputs are derived from explicit feedback from the user and inferred feedback from the user's behavior during the edit session. A reward score, based on anti-unification and provenance analysis, is used to classify the additional inputs as either a positive input or a negative input. Outputs are generated for the positive inputs that are consistent with the existing examples and then used to synthesize a new program transformation. The program transformations are then used to generate code edit suggestions during the edit session.

Feedback-driven semi-supervised synthesis of program transformations

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San Diego, CA, United States of America

Shraddha Govind Barke