Abstract: Techniques for generating a schema transformation for application data to monitor and manage the application in a runtime environment are disclosed. A system runs an application plugin in a runtime environment to identify data generated by application modules in one or both of an application build process and an application execution process. The application plugin is a software program executed together with the application build process. The application plugin identifies a source schema associated with application data. The application plugin identifies a target schema associated with an analysis program or machine learning model. The application plugin generates a schema transformation to convert application runtime data into a target data set. The system applies the target data set to an analysis program, such as a machine learning model, to generate output analysis data associated with the application.

Abstract: Techniques for managing a software build using a machine learning model are disclosed. A system obtains historical data associated with historical software builds. The historical data includes attribute data for a plurality of development stages associated with a historical software build and labels indicating success or failure for the plurality of development stages. The system trains a machine learning model using the historical data associated with the historical software builds to generate predictions of success or failure of the plurality of development stages. The system receives attributes of a target software build and a selection of a first target development stage of the target software build. The system applies the machine learning model to the target software build to generate a first prediction of success or failure of the first target development stage.

Abstract: A main build environment may create a container for the main source code and any required utilities when building a software application. This container may be populated with an operating system, a configuration that includes instructions for performing the build, toolchains needed for the build, and/or other utilities. If any dependencies on third-party software modules exist, the build environment may create a new container and import the source code for the software module instead of simply importing the precompiled software modules into the build environment. The new container may similarly include toolchains and a configuration specific to building the software module. The software module may then be built within its separate container to generate an executable artifact that may be uploaded/imported into the main build environment during the main build process. Software dependencies can thus be built as part of the main build process from source code.

Abstract: Techniques for managing a software build using a machine learning model are disclosed. A system obtains historical data associated with historical software builds. The historical data includes attribute data for a plurality of development stages associated with a historical software build and labels indicating success or failure for the plurality of development stages. The system trains a machine learning model using the historical data associated with the historical software builds to generate predictions of success or failure of the plurality of development stages. The system receives attributes of a target software build and a selection of a first target development stage of the target software build. The system applies the machine learning model to the target software build to generate a first prediction of success or failure of the first target development stage.

Abstract: A faceted reflector (32, 32?) for receiving an incident radiation beam (2) and directing a reflected radiation beam at a target. The faceted reflector comprises a plurality of facets, each of the plurality of facets comprising a reflective surface. The reflective surfaces of each of a first subset of the plurality of facets define respective parts of a first continuous surface and are arranged to reflect respective first portions of the incident radiation beam in a first direction to provide a first portion of the reflected radiation beam. The reflective surfaces of each of a second subset of the plurality of facets define respective parts of a second continuous surface and are arranged to reflect respective second portions of the incident radiation beam in a second direction to provide a second portion of the reflected radiation beam.

Abstract: A faceted reflector (32, 32?) for receiving an incident radiation beam (2) and directing a reflected radiation beam at a target. The faceted reflector comprises a plurality of facets, each of the plurality of facets comprising a reflective surface. The reflective surfaces of each of a first subset of the plurality of facets define respective parts of a first continuous surface and are arranged to reflect respective first portions of the incident radiation beam in a first direction to provide a first portion of the reflected radiation beam. The reflective surfaces of each of a second subset of the plurality of facets define respective parts of a second continuous surface and are arranged to reflect respective second portions of the incident radiation beam in a second direction to provide a second portion of the reflected radiation beam.

Abstract: A mark used in the determination of overlay error comprises sub-features, the sub-features having a smallest pitch approximately equal to the smallest pitch of the product features. The sensitivity to distortions and aberrations is similar as that for the product features. When the mark is developed the sub-features merge and the outline of the larger feature is developed.