Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: The invention relates to a method of forming at least one nano-structure with a reusable template structure having a channel. The method includes introducing at least one reagent into the channel, and reacting the at least one reagent to form a nano-structure within the channel. The nano-structure forming channel may be positioned in alignment with one or more electrode structures, which may be positioned within or upon the substrate, may be embedded in the reusable template structure, and/or may be external electrode structures positioned outside of the reusable template structure and independent of the substrate. In addition, the electrode structures may be a source material for the formation of the nano-structure in the channel.