Amazon Technologies, Inc.

Pranav Garg

Omer Tripp

Vineet Shashikant Chaoji

Sengamedu Hanumantha Rao Srinivasan

Srinivasan Sengamedu Hanumantha Rao

Rajdeep Mukherjee

Hoan Nguyen

Jasmeet Chhabra

Baris Coskun

Saurabh Sohoney

Hakimuddin Hanif

Shishir Sethiya

Karthik Sundaresan Gurumoorthy

Sahil Sareen

Neela Sawant

Himani Khanduja

Benjamin Robert Liblit

Abhin Sharma

Alok Dhamanaskar

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 generating custom rules are described. For example, a system to receive at least one request to create rules based on a policy and code repository files stored by the storage service; analyze the policy to generate a collection of rule candidates; analyze the code repository files to identify labeled code examples that either conform or do not conform to the rule candidates; receive a selection of the labeled code examples; and synthesize at least one rule that includes a precondition that specifies applicability to the selected labeled code examples and a postcondition that expresses a check to be performed contingent on the precondition being satisfied is at least described.

Rule creation for code analysis

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

Disclosed are various embodiments for automatically identifying whether applications are static or dynamic. In one embodiment, code of an application is analyzed to determine instances of requesting data via a network in the application. Characteristics of the instances of requesting data via the network are provided to a machine learning model. The application is automatically classified as either dynamic or static according to the machine learning model.

Automatically identifying dynamic applications

Techniques for performing machine learning-based program analysis using synthetically generated labeled data are described. A method of performing machine learning-based program analysis using synthetically generated labeled data may include receiving a request to perform program analysis on code, determining a first portion of the code associated with a first error type, sending the first portion of the code to an endpoint of a machine learning service associated with an error detection model to detect the first error type, the error detection model trained using synthetically generated labeled data, and receiving inference results from the error detection model identifying one or more errors of the first error type in the first portion of the code.

Machine learning-based program analysis using synthetically generated labeled data

Techniques for monitoring a computing environment for anomalous activity are presented. An example method includes receiving a request to invoke an action within the computing environment. An anomaly score is generated for the received request by applying a probabilistic model to properties of the request. The anomaly score generally indicates a likelihood that the properties of the request correspond to historical activity within the computing environment for a user associated with the request. The probabilistic model generally comprises a model having been trained using historical activity within the computing environment for a plurality of users, the historical activity including information identifying an action performed in the computing environment and contextual information about a historical request. Based on the generated anomaly score, one or more actions are taken to process the request such that execution of requests having anomaly scores indicative of unexpected activity may be blocked pending confirmation.

Scoring events using noise-contrastive estimation for anomaly detection

At an artificial intelligence based service to detect violations of resource usage policies, an indication of a first data set comprising a plurality of network traffic flow records associated with at least a first device of a set of devices may be obtained. Using the first data set, a machine learning model may be trained to predict whether resource usage of a particular device of a particular network violates a first resource usage acceptability criterion. In response to determining, using a trained version of the model, that the probability that a second device has violated the acceptability criterion exceeds a threshold, one or more actions responsive to the violation may be initiated.

Artificial intelligence system for network traffic flow based detection of service usage policy violations

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

Methods, systems, and computer-readable media for accurately estimating causal effects for related events are disclosed. A plurality of estimates of causal effects of events are determined. The estimates are determined independently. A subset of the estimates are determined not to satisfy a relationship among the causal effects. A set of accurate estimates are generated based at least in part on the subset of the estimates. The accurate estimates are generated using a smoothing process, and the accurate estimates satisfy the relationship.

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Jersey City, NJ, United States of America

Pranav Garg