Amazon Technologies, Inc.

Hoan Nguyen

Srinivasan Sengamedu Hanumantha Rao

Omer Tripp

Pranav Garg

Neela Sawant

Jasmeet Chhabra

Sengamedu Hanumantha Rao Srinivasan

Rajdeep Mukherjee

Hakimuddin Hanif

Shishir Sethiya

Anton Emelyanov

Sahil Sareen

Alok Dhamanaskar

Himani Khanduja

Harshit Gupta

Xiaoxin Zhao

Code changes may be hierarchically clustered to discover coding practices. Code change graphs for changes to code in a source code repository may be clustered according to hierarchy of different features determined for the source code into groups. The code change graphs in the groups may then be indexed according their similarity with other code change graphs in the groups. Then one or more coding practices corresponding to the indexed code changes may be provided.

Hierarchical clustering for coding practice discovery

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 generating rules from documentation are described. For example, a method for generating rules may include generating one or more rules from documentation by: extracting a plurality of chunks from the documentation, inferring one or more candidate rules from the extracted chunks, mining the inferred one or more candidate rules to determine at least one of the one or more candidate rules is to be included in rule generation, classifying the at least one mined one or more candidate rules as one or more rules, and extracting information to generate the one or more rules.

Mining software specification from online documentation

Techniques for generating rules from documentation are described. For example, a method for generating rules may include generating one or more templates containing patterns and anti-patterns from code of one or more documents, wherein a pattern captures a good coding practice as detailed in the documents and an anti-pattern is a proper subset of a pattern that does not include a construct described in the documents as being a recommended and/or required usage; constructing one or more graphs from the one or more templates; mining the constructed one or more graphs to find target sub-graphs which do not contain bugs, wherein a bug occurs when an anti-pattern matches, but a pattern does not match; comparing the target sub-graphs to a representative dataset to remove target sub-graphs that violate good usage; and codifying the sub-graphs that represent good usage.

Codifying rules from online documentation

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Bothell, WA, United States of America

Hoan Nguyen