Amazon Technologies, Inc.

Shishir Sethiya

Srinivasan Sengamedu Hanumantha Rao

Omer Tripp

Jasmeet Chhabra

Pranav Garg

Neela Sawant

Hoan Nguyen

Hakimuddin Hanif

Russell Reas

Anton Emelyanov

Sahil Sareen

Himani Khanduja

Alok Dhamanaskar

Harshit Gupta

Xiaoxin Zhao

Yinglong Wang

Techniques for a code reviewer service to provide recommendations on source code are described. A code reviewer service may run rules and/or machine learning models to provide the recommendations. A machine learning model may identify one or more predicted issues of source code, and the code reviewer service may provide one or more recommendations based at least in part on the one or more predicted issues. Code reviewer service may allow a pull request for a code repository to trigger the generation of recommendations for the source code in the repository. The recommendations may be posted on the pull request.

Customizable service for recommendations on source code quality

Techniques for unsupervised learning of embeddings on source code from non-local contexts are described. Code can be processed to generate an abstract syntax tree (AST) which represents syntactic paths between tokens in the code. Once the AST(s) have been generated, the paths in the AST(s) can be crawled to identify terminals (e.g., leaf nodes in the AST) and paths between terminals can be identified. The pairs of tokens identified at the ends of each path can then be used to generate a cooccurrence matrix. For example, if X number of unique terminals are identified, a matrix of size X by X can be generated to indicate a frequency at which pairs of terminals cooccur. This cooccurrence matrix can then be used as input to existing techniques for learning vector-space embeddings, such as word2vec, GloVe, Swivel, etc.

Techniques for unsupervised learning embeddings on source code tokens from non-local contexts

Growing community of inventors

Seattle, WA, United States of America

Shishir Sethiya