Amazon Technologies, Inc.

Sengamedu Hanumantha Rao Srinivasan

Omer Tripp

Pranav Garg

Rajdeep Mukherjee

Qiang Zhou

Neela Sawant

Hangqi Zhao

Willem Conradie Visser

Hoan Nguyen

Benjamin Robert Liblit

Abhin Sharma

Session-specific edit recommendations may be made for editing a code file. After a code editing session is started, code file edits may be captured. A machine learning technique may be applied to learn the code edits and recommend alternative code portions for portions of the code file during the code editing session. The recommendations may be provided and accepted, or not, via an interface of a code editor application.

Learning session-specific code recommendations for editing code files

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

A system identifies a pattern in source code. The pattern is identified based, at least in part, on correlation between units of code, or on the derivation of a rule from a recurring sequence in the source code. The system identifies a portion of the source code that at least partially matches the pattern, and determines that this portion includes at least one deviation from the pattern. The system then generates an error message to describe the deviation.

Inconsistency-based bug detection

Techniques for providing a visual code review editor are described. An electronic device is caused to display a graphical user interface including an editor portion to edit code review rules used by a code review service of a cloud provider network. The editor portion of the graphical user interface is caused to display a first graph associated with a first code review rule, the first graph including a first node, a second node, and a first edge connecting the first node and the second node. An indication that a third node has been added to the graph via the editor portion of the graphical user interface is received. The first code review rule is updated by the code review service to reflect the addition of the third node, the first code review rule is in a text format.

Visual query language for code review rules

An aggregate representation of a collection of source code examples is constructed. The collection includes positive examples that conform to a coding practice and negative examples do not conform to the coding practice. The aggregate representation includes nodes corresponding to source code elements, and edges representing relationships between code elements. Using an iterative analysis of the aggregate representation, a rule to automatically detect non-conformance is generated. The rule is used to provide an indication that a set of source code is non-conformant.

Example-based synthesis of rules for detecting violations of software coding practices

Code may be analyzed according to natural language descriptions of coding practices. A practice for code written in a natural language description may be received. An embedding of the natural language description may be generated using a machine learning model trained to detect examples of practices. The embedding may be compared with embeddings of code portions stored in an index to detect one or more portions of code that satisfy a facet of the practice. The detected portions of code may be identified.

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Seattle, WA, United States of America

Sengamedu Hanumantha Rao Srinivasan