Abstract: Contextualized selection of components in one aspect may include generating a developer profile associated with a team member, for example, for all team members designated to work on a computer-implemented development project, and generating a component profile associated with a software component, for example, for all software components identified based on received requirement specification associated with the project. Applicability of a software component to the team members may be computed based on developer profiles and a component profile associated with the software component. Based on the applicability, components are selected and may be downloaded to build a team project component repository.

Abstract: Focus requests to display applications are received, each focus request associated with at least one application from the applications. Application context from metadata tags describing content of the application is received. User context associated with information about a status of a user interacting with the computer system is received. Display priority of the applications based on the application context and the user context is determined. A list of the applications based on the display priority is created. The applications are displayed in windows in the graphical user interface environment based on the list.

Abstract: Focus requests to display applications are received, each focus request associated with at least one application from the applications. Application context from metadata tags describing content of the application is received. User context associated with information about a status of a user interacting with the computer system is received. Display priority of the applications based on the application context and the user context is determined. A list of the applications based on the display priority is created. The applications are displayed in windows in the graphical user interface environment based on the list.

Abstract: Techniques are provided for performing automated operations to determine maturity of a specified project. Information is received regarding each of a plurality of artifacts associated with the project, such as project documentation, source code repositories, and a tracked issue database for the project. A data sufficiency level associated with each provided artifact is determined, and each artifact is provided to one or more of multiple analysis engines. The analysis engines are executed to produce one or more weighted feature vectors for each of the artifacts associated with the specified project, and input to a prediction engine in order to provide a maturity rating for the project based on the weighted feature vectors.

Abstract: Techniques are provided for performing automated operations to determine maturity of a specified project. Information is received regarding each of a plurality of artifacts associated with the project, such as project documentation, source code repositories, and a tracked issue database for the project. A data sufficiency level associated with each provided artifact is determined, and each artifact is provided to one or more of multiple analysis engines. The analysis engines are executed to produce one or more weighted feature vectors for each of the artifacts associated with the specified project, and input to a prediction engine in order to provide a maturity rating for the project based on the weighted feature vectors.

Abstract: Contextualized selection of components in one aspect may include generating a developer profile associated with a team member, for example, for all team members designated to work on a computer-implemented development project, and generating a component profile associated with a software component, for example, for all software components identified based on received requirement specification associated with the project. Applicability of a software component to the team members may be computed based on developer profiles and a component profile associated with the software component. Based on the applicability, components are selected and may be downloaded to build a team project component repository.

Abstract: Focus requests to display applications are received, each focus request associated with at least one application from the applications. Application context from metadata tags describing content of the application is received. User context associated with information about a status of a user interacting with the computer system is received. Display priority of the applications based on the application context and the user context is determined. A list of the applications based on the display priority is created. The applications are displayed in windows in the graphical user interface environment based on the list.

Abstract: Focus requests to display applications are received, each focus request associated with at least one application from the applications. Application context from metadata tags describing content of the application is received. User context associated with information about a status of a user interacting with the computer system is received. Display priority of the applications based on the application context and the user context is determined. A list of the applications based on the display priority is created. The applications are displayed in windows in the graphical user interface environment based on the list.

Abstract: An application provisioning model is automatically created. The model is created from a high-level application and specifies dependencies of the application. It is used to provision the application on one or more nodes or other actions.

Abstract: Techniques for performing capacity planning for applications running on a computational infrastructure are provided. The techniques include instrumenting an application under development to receive one or more performance metrics under a physical deployment plan, receiving the one or more performance metrics from the computational infrastructure hosting one or more applications that are currently running, using a predictive inference engine to determine how the application under development can be deployed, and using the determination to perform capacity planning for the applications on the computational infrastructure.

Abstract: Techniques for performing capacity planning for applications running on a computational infrastructure are provided. The techniques include instrumenting an application under development to receive one or more performance metrics under a physical deployment plan, receiving the one or more performance metrics from the computational infrastructure hosting one or more applications that are currently running, using a predictive inference engine to determine how the application under development can be deployed, and using the determination to perform capacity planning for the applications on the computational infrastructure.

Abstract: An application provisioning model is automatically created. The model is created from a high-level application and specifies dependencies of the application. It is used to provision the application on one or more nodes or other actions.