Abstract: Techniques are described herein that are capable of performing automatic risk analysis of software. An automatic risk analyzer may determine correlations between binaries of the software and source files of the software, between the source files and functions (i.e., sub-routines) of the software, between changes to source code of the software and defects, new features, and/or feature enhancements in the software, between attributes of the software (e.g., attributes associated with the changes to the source code), etc. at various time instances. The automatic risk analyzer may compare the correlations corresponding to a first time instance and the correlations corresponding to a second time instance to determine any of a variety of temporal relationships regarding underlying factors, including but not limited to new feature(s), feature enhancement(s), defect(s), churn rate, code coverage, code complexity, etc.

Abstract: Techniques are described herein that are capable of performing automatic risk analysis of software. An automatic risk analyzer may determine correlations between binaries of the software and source files of the software, between the source files and functions (i.e., sub-routines) of the software, between changes to source code of the software and defects, new features, and/or feature enhancements in the software, between attributes of the software (e.g., attributes associated with the changes to the source code), etc. at various time instances. The automatic risk analyzer may compare the correlations corresponding to a first time instance and the correlations corresponding to a second time instance to determine any of a variety of temporal relationships regarding underlying factors, including but not limited to new feature(s), feature enhancement(s), defect(s), churn rate, code coverage, code complexity, etc.

Abstract: Techniques are described herein that are capable of performing automatic risk analysis of software. An automatic risk analyzer may determine correlations between binaries of the software and source files of the software, between the source files and functions (i.e., sub-routines) of the software, between changes to source code of the software and defects, new features, and/or feature enhancements in the software, between attributes of the software (e.g., attributes associated with the changes to the source code), etc. at various time instances. The automatic risk analyzer may compare the correlations corresponding to a first time instance and the correlations corresponding to a second time instance to determine any of a variety of temporal relationships regarding underlying factors, including but not limited to new feature(s), feature enhancement(s), defect(s), churn rate, code coverage, code complexity, etc.

Abstract: Techniques are described herein that are capable of performing automatic risk analysis of software. An automatic risk analyzer may determine correlations between binaries of the software and source files of the software, between the source files and functions (i.e., sub-routines) of the software, between changes to source code of the software and defects, new features, and/or feature enhancements in the software, between attributes of the software (e.g., attributes associated with the changes to the source code), etc. at various time instances. The automatic risk analyzer may compare the correlations corresponding to a first time instance and the correlations corresponding to a second time instance to determine any of a variety of temporal relationships regarding underlying factors, including but not limited to new feature(s), feature enhancement(s), defect(s), churn rate, code coverage, code complexity, etc.

Abstract: Techniques are described herein that are capable of performing automatic risk analysis of software. An automatic risk analyzer may determine correlations between binaries of the software and source files of the software, between the source files and functions (i.e., sub-routines) of the software, between changes to source code of the software and defects, new features, and/or feature enhancements in the software, between attributes of the software (e.g., attributes associated with the changes to the source code), etc. at various time instances. The automatic risk analyzer may compare the correlations corresponding to a first time instance and the correlations corresponding to a second time instance to determine any of a variety of temporal relationships regarding underlying factors, including but not limited to new feature(s), feature enhancement(s), defect(s), churn rate, code coverage, code complexity, etc.

Abstract: Methods, systems, and computer-readable media are disclosed for monitoring a distributed application. A particular method identifies a plurality of components of a distributed application. The method also identifies a specific technology associated with a particular component and attaches a technology specific interceptor to the particular component based on the identified specific technology. The method includes intercepting messages that are sent by or received by the particular component using the technology specific interceptor. At least one potential work item is generated based on the intercepted messages. The method includes determining whether to schedule the at least one potential work item for execution based on a predicted impact of the at least one work potential item on performance of the distributed application.

Abstract: Techniques are described herein that are capable of performing automatic risk analysis of software. An automatic risk analyzer may determine correlations between binaries of the software and source files of the software, between the source files and functions (i.e., sub-routines) of the software, between changes to source code of the software and defects, new features, and/or feature enhancements in the software, between attributes of the software (e.g., attributes associated with the changes to the source code), etc. at various time instances. The automatic risk analyzer may compare the correlations corresponding to a first time instance and the correlations corresponding to a second time instance to determine any of a variety of temporal relationships regarding underlying factors, including but not limited to new feature(s), feature enhancement(s), defect(s), churn rate, code coverage, code complexity, etc.

Abstract: Methods, systems, and computer-readable media are disclosed for monitoring a distributed application. A particular method identifies a plurality of components of a distributed application. The method also identifies a specific technology associated with a particular component and attaches a technology specific interceptor to the particular component based on the identified specific technology. The method includes intercepting messages that are sent by or received by the particular component using the technology specific interceptor. At least one potential work item is generated based on the intercepted messages. The method includes determining whether to schedule the at least one potential work item for execution based on a predicted impact of the at least one work potential item on performance of the distributed application.

Abstract: Methods, systems, and computer-readable media are disclosed for discovering components and dependencies in a distributed computing environment. A particular method includes discovering a first component and a second component of a distributed computing environment, where the first component and the second component are associated with different discoverable technologies. Each component is discovered based on a match between a characteristic of the component and a technology specification associated with the discoverable technology. The method also includes discovering a dependency between the first component and the second component.

Abstract: Disclosed are systems and methods for implementing a unified console for managing computing-based devices. Described is a unified management solution to efficiently manage one or more workloads or a plurality of tasks performed by a user for managing the resources of applications in a computer network that includes the computing-based devices. The system can be implemented by providing a combined functionality that includes features of monitoring and update services, in a single console.