Red Hat, Inc.

Christoph Goern

Fridolin Pokorny

Daniel W Riek

Francesco Murdaca

Jay Vyas

Daniel J Walsh

Ian Scott McLeod

Subin Modeel

Vaclav Pavlin

Langdon White

Sanjay Arora

Aaron Weitekamp

Harshad Reddy Nalla

Michael Virgil

A build process for building software in a target environment can be generated using a system described herein. For example, the system can receive a recommended software-stack for a target software item to be built in a target build environment. The system can determine, based on the recommended software-stack, a build process for building the target software item in the target build environment. The system may then execute the build process to generate a software build of the target software item in the target build environment.

Generating a build process for building software in a target environment

A pipeline can be constructed for implementing a software-stack resolution process. For example, a system can receive a request from a client device for a recommended software-stack for a target software item. The system can also receive pipeline configuration data specifying configurable pipeline units to be included in the pipeline. The pipeline can include a search process for identifying and analyzing a group of software-stack candidates associated with the target software item. The system can construct the pipeline using the configurable pipeline units based on the pipeline configuration data. One or more of the configurable pipeline units can be arranged in the pipeline to guide the search process by adjusting one or more parameters of the search process. The system can then execute the pipeline and transmit a response to the client device indicating a recommended software-stack for the target software item.

Constructing pipelines for implementing a software-stack resolution process

A method for analyzing performance impacts of changes to source code includes logging a change to source code and an identifier (ID) of an author of the change into a database. The method further includes providing the change to the source code to a machine learning model trained to analyze performance impacts of source-code changes. The method further includes generating, by a processing device, in view of the machine learning model, a performance impact of the change to the source code. The method further includes determining that the performance impact is above a performance-impact threshold. The method further includes, in view of the determining, preventing the change to the source code from being executed by the processing device.

Analyzing performance impacts of source code changes

A searchable database of software features for software projects can be automatically built in some examples. One such example can involve analyzing descriptive information about a software project to determine software features of the software project. Then a feature vector for the software project can be generated based on the software features of the software project. The feature vector can be stored in a database having multiple feature vectors for multiple software projects. The multiple feature vectors can be easily and quickly searched in response to search queries.

Automatically building a searchable database of software features for software projects

A recommended software-stack can be determined for a target software item. For example, a system can receive an input specifying a target software item and a characteristic of a computing environment in which the target software item is to be executed. The system can then generate software-stack candidates for the target software item, the software-stack candidates having unique configurations of software components. The system can determine a respective score for each software-stack candidate of the software-stack candidates based on the characteristic of the computing environment and a unique configuration of software components forming the software-stack candidate. The system can select a particular software-stack candidate from the software-stack candidates as a recommended software-stack, based on the respective score for the particular software-stack candidate having a predefined attribute. The system can then generate an output indicating the recommended software-stack to enable the recommended software-stack to be included in the computing environment.

Determining a recommended software-stack for a target software item

Methods, systems, and computer program products are included for deploying a composite application. A method includes receiving, at a computing device, a container image that includes a nulecule. The nulecule is parsed to identify a first service, a second service, and a parameter of the first service. An input value is received corresponding to the parameter of the first service. A configuration value of the first service is modified to include the input value. The first service and the second service are deployed on the computing device, with the first service being configured with the configuration value.

Active representation of a composite application

An example method includes creating a builder image on a node. The builder image is based on an application, a combination of application dependencies of the application, and a set of node features of the node. The method also includes determining a set of optimized libraries corresponding to the combination of application dependencies and to the node. The method further includes creating, based on the builder image and the set of optimized libraries, an application runtime container. The method also includes running the application and the set of optimized libraries in the application runtime container.

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Bonn, Germany

Christoph Goern