Abstract: This document describes modeling and simulation techniques to select a cloud architecture profile based on correlations between application workloads and resource utilization. In some aspects, a method includes obtaining infrastructure data specifying utilization of computing resources of an existing computing system. Application workload data specifying tasks performed by one or more applications running on the existing computing system is obtained. One or more models are generated based on the infrastructure data and the application workload data. The model(s) define an impact on utilization of each computing resource in response to changes in workloads of the application(s). A workload is simulated, using the model(s), on a candidate cloud architecture profile that specifies a set of computing resources. A simulated utilization of each computing resource of the candidate cloud architecture profile is determined based on the simulation.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for test cycle optimization using contextual association mapping. In one aspect, a method includes obtaining an artifact that includes a collection of reference items, where each reference item includes a sequence of words, generating candidate tags from each of the reference items based on the sequences of words in the reference items, selecting a subset of the candidate tags as context tags based on an amount that the candidate tags appear in the reference items, obtaining a sample item that includes a sequence of words, identifying a subset of the context tags in the sequence of words in the sample item, and classifying a subset of the reference items as contextually similar to the sample item based the context tags that were identified.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for test cycle optimization using contextual association mapping. In one aspect, a method includes obtaining an artifact that includes a collection of reference items, where each reference item includes a sequence of words, generating candidate tags from each of the reference items based on the sequences of words in the reference items, selecting a subset of the candidate tags as context tags based on an amount that the candidate tags appear in the reference items, obtaining a sample item that includes a sequence of words, identifying a subset of the context tags in the sequence of words in the sample item, and classifying a subset of the reference items as contextually similar to the sample item based the context tags that were identified.

Abstract: This document describes modeling and simulation techniques to select a cloud architecture profile based on correlations between application workloads and resource utilization. In some aspects, a method includes obtaining infrastructure data specifying utilization of computing resources of an existing computing system. Application workload data specifying tasks performed by one or more applications running on the existing computing system is obtained. One or more models are generated based on the infrastructure data and the application workload data. The model(s) define an impact on utilization of each computing resource in response to changes in workloads of the application(s). A workload is simulated, using the model(s), on a candidate cloud architecture profile that specifies a set of computing resources. A simulated utilization of each computing resource of the candidate cloud architecture profile is determined based on the simulation.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for test cycle optimization using contextual association mapping. In one aspect, a method includes obtaining an artefact that includes a collection of reference items, where each reference item includes a sequence of words, generating candidate tags from each of the reference items based on the sequences of words in the reference items, selecting a subset of the candidate tags as context tags based on an amount that the candidate tags appear in the reference items, obtaining a sample item that includes a sequence of words, identifying a subset of the context tags in the sequence of words in the sample item, and classifying a subset of the reference items as contextually similar to the sample item based the context tags that were identified.

Abstract: This document describes modeling and simulation techniques to select a cloud architecture profile based on correlations between application workloads and resource utilization. In some aspects, a method includes obtaining infrastructure data specifying utilization of computing resources of an existing computing system. Application workload data specifying tasks performed by one or more applications running on the existing computing system is obtained. One or more models are generated based on the infrastructure data and the application workload data. The model(s) define an impact on utilization of each computing resource in response to changes in workloads of the application(s). A workload is simulated, using the model(s), on a candidate cloud architecture profile that specifies a set of computing resources. A simulated utilization of each computing resource of the candidate cloud architecture profile is determined based on the simulation.

Abstract: This document describes modeling and simulation techniques to select a cloud architecture profile based on correlations between application workloads and resource utilization. In some aspects, a method includes obtaining infrastructure data specifying utilization of computing resources of an existing computing system. Application workload data specifying tasks performed by one or more applications running on the existing computing system is obtained. One or more models are generated based on the infrastructure data and the application workload data. The model(s) define an impact on utilization of each computing resource in response to changes in workloads of the application(s). A workload is simulated, using the model(s), on a candidate cloud architecture profile that specifies a set of computing resources. A simulated utilization of each computing resource of the candidate cloud architecture profile is determined based on the simulation.

Abstract: A method and system for fault analysis and prediction in an enterprise environment is described. In one embodiment, a method includes obtaining data from a plurality of sources in the enterprise environment. The plurality of sources includes at least one or more systems, users, or applications. The obtained data is associated with identifiers that include a theme selected from a set of themes and one or more keywords that are specific to each theme. The method includes generating a workflow for a user based on a session identifier and/or timestamps associated with activity by the user. The workflow identifies a time-based sequence of interactions by the user with the at least one or more systems or applications in the enterprise environment. The method also includes determining at least one fault identification or fault prediction based on the workflow and identifiers associated with the obtained data that corresponds to the workflow.

Abstract: A method and system for fault analysis and prediction in an enterprise environment is described. In one embodiment, a method includes obtaining data from a plurality of sources in the enterprise environment. The plurality of sources includes at least one or more systems, users, or applications. The obtained data is associated with identifiers that include a theme selected from a set of themes and one or more keywords that are specific to each theme. The method includes generating a workflow for a user based on a session identifier and/or timestamps associated with activity by the user. The workflow identifies a time-based sequence of interactions by the user with the at least one or more systems or applications in the enterprise environment. The method also includes determining at least one fault identification or fault prediction based on the workflow and identifiers associated with the obtained data that corresponds to the workflow.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for test cycle optimization using contextual association mapping. In one aspect, a method includes obtaining an artefact that includes a collection of reference items, where each reference item includes a sequence of words, generating candidate tags from each of the reference items based on the sequences of words in the reference items, selecting a subset of the candidate tags as context tags based on an amount that the candidate tags appear in the reference items, obtaining a sample item that includes a sequence of words, identifying a subset of the context tags in the sequence of words in the sample item, and classifying a subset of the reference items as contextually similar to the sample item based the context tags that were identified.

Abstract: An automatic correlation accelerator tool may access at least a first recording and a second recording of a base script that defines operations executed in testing performance of a system. The tool may cause the system to execute the first recording of the base script and the second recording of the base script and store, in electronic storage, dynamic value data that describes dynamic values generated during execution of the first recording of the base script and during execution of the second recording of the base script. The tool automatically, without human intervention, analyzes the stored dynamic value data to identify candidates for correlation within the base script and generates a correlated script based on the identified candidates for correlation and the base script.

Abstract: Automatic correlation, in which an automatic correlation accelerator tool accesses at least a first and a second recording of a base script that defines operations executed in testing performance of a system. The tool causes the system to execute the first recording of the base script and the second recording of the base script and stores, in electronic storage, dynamic value data that describes dynamic values generated during execution of the first recording of the base script and during execution of the second recording of the base script. The tool automatically, without human intervention, analyzes the stored dynamic value data to identify candidates for correlation within the base script and generates a correlated script based on the identified candidates for correlation and the base script.

Abstract: Automatic correlation, in which an automatic correlation accelerator tool accesses at least a first and a second recording of a base script that defines operations executed in testing performance of a system. The tool causes the system to execute the first recording of the base script and the second recording of the base script and stores, in electronic storage, dynamic value data that describes dynamic values generated during execution of the first recording of the base script and during execution of the second recording of the base script. The tool automatically, without human intervention, analyzes the stored dynamic value data to identify candidates for correlation within the base script and generates a correlated script based on the identified candidates for correlation and the base script.

Abstract: Automatic correlation, in which an automatic correlation accelerator tool accesses at least a first and a second recording of a base script that defines operations executed in testing performance of a system. The tool causes the system to execute the first recording of the base script and the second recording of the base script and stores, in electronic storage, dynamic value data that describes dynamic values generated during execution of the first recording of the base script and during execution of the second recording of the base script. The tool automatically, without human intervention, analyzes the stored dynamic value data to identify candidates for correlation within the base script and generates a correlated script based on the identified candidates for correlation and the base script.

Abstract: Automatic correlation, in which an automatic correlation accelerator tool accesses at least a first and a second recording of a base script that defines operations executed in testing performance of a system. The tool causes the system to execute the first recording of the base script and the second recording of the base script and stores, in electronic storage, dynamic value data that describes dynamic values generated during execution of the first recording of the base script and during execution of the second recording of the base script. The tool automatically, without human intervention, analyzes the stored dynamic value data to identify candidates for correlation within the base script and generates a correlated script based on the identified candidates for correlation and the base script.

Abstract: Automatic correlation, in which an automatic correlation accelerator tool accesses at least a first and a second recording of a base script that defines operations executed in testing performance of a system. The tool causes the system to execute the first recording of the base script and the second recording of the base script and stores, in electronic storage, dynamic value data that describes dynamic values generated during execution of the first recording of the base script and during execution of the second recording of the base script. The tool automatically, without human intervention, analyzes the stored dynamic value data to identify candidates for correlation within the base script and generates a correlated script based on the identified candidates for correlation and the base script.