Abstract: The method includes identifying a test report log for a regression test. The method further includes identifying one or more errors in the identified test report log. The method further includes determining a severity category for the one or more identified errors in the identified test report log. The method further includes determining a severity category for the identified test report log based on the determined severity category for the one or more identified errors in the identified test report log.

Abstract: The method includes identifying a test report log for a regression test. The method further includes identifying one or more errors in the identified test report log. The method further includes determining a severity category for the one or more identified errors in the identified test report log. The method further includes determining a severity category for the identified test report log based on the determined severity category for the one or more identified errors in the identified test report log.

Abstract: Context-based coordinated data retrieval for mobile devices is provided. An interface between application code of a mobile device and an operating system of the mobile device receives a declarative language based data-retrieval request made by the application code for data from an I/O device of the mobile device. Based on operating context of the mobile device, opportunistic coordination rule(s) are selected from a local rules cache, the rule(s) representative of at least one power-saving option for retrieving data for responding to the request. The rule(s) are evaluated to determine how to retrieve data for responding to the request, and action(s) are performed based on the evaluating, the action(s) including responding to the request.

Abstract: The method includes identifying a test report log for a regression test. The method further includes identifying one or more errors in the identified test report log. The method further includes determining a severity category for the one or more identified errors in the identified test report log. The method further includes determining a severity category for the identified test report log based on the determined severity category for the one or more identified errors in the identified test report log.

Abstract: The method includes identifying a test report log for a regression test. The method further includes identifying one or more errors in the identified test report log. The method further includes determining a severity category for the one or more identified errors in the identified test report log. The method further includes determining a severity category for the identified test report log based on the determined severity category for the one or more identified errors in the identified test report log.

Abstract: The method includes identifying a test report log for a regression test. The method further includes identifying one or more errors in the identified test report log. The method further includes determining a severity category for the one or more identified errors in the identified test report log. The method further includes determining a severity category for the identified test report log based on the determined severity category for the one or more identified errors in the identified test report log.

Abstract: An approach is provided for tuning middleware. Performance-related settings are loaded. Performance data of the middleware of a MobileFirst Platform (MFP) running in a docker container infrastructure is received. The performance data is collected by agents installed in container groups. Based on the performance data, a performance issue in one of the container groups is identified and a server included in the one container group is identified as a source of the performance issue. Recommendations are generated for tuning the middleware by modifying one or more of the performance-related settings. While the middleware is running in the docker container infrastructure, one of the recommendations is applied to modify one of the performance-related settings which dynamically tunes the middleware, thereby resolving the performance issue.

Abstract: Context-based coordinated data retrieval for mobile devices is provided. An interface between application code of a mobile device and an operating system of the mobile device receives a declarative language based data-retrieval request made by the application code for data from an I/O device of the mobile device. Based on operating context of the mobile device, opportunistic coordination rule(s) are selected from a local rules cache, the rule(s) representative of at least one power-saving option for retrieving data for responding to the request. The rule(s) are evaluated to determine how to retrieve data for responding to the request, and action(s) are performed based on the evaluating, the action(s) including responding to the request.

Abstract: Context-based coordinated data retrieval for mobile devices is provided. An interface between application code of a mobile device and an operating system of the mobile device receives a declarative language based data-retrieval request made by the application code for data from an I/O device of the mobile device. Based on operating context of the mobile device, opportunistic coordination rule(s) are selected from a local rules cache, the rule(s) representative of at least one power-saving option for retrieving data for responding to the request. The rule(s) are evaluated to determine how to retrieve data for responding to the request, and action(s) are performed based on the evaluating, the action(s) including responding to the request.

Abstract: A computer-implemented method, system, and/or computer program product improves a functionality of a computing device by optimizing improvements to an application running on the computing device. Defects in an application identified by user reviews are prioritized by one or more processors to create a representational model of user reviews. A rating improvement to the application caused by changing the application to resolve complaints represented by top-k negative review representations that are clustered within the predetermined distance from the defect representation is predicted by the processor(s).

Abstract: The method includes identifying a test report log for a regression test. The method further includes identifying one or more errors in the identified test report log. The method further includes determining a severity category for the one or more identified errors in the identified test report log. The method further includes determining a severity category for the identified test report log based on the determined severity category for the one or more identified errors in the identified test report log.

Abstract: The method includes identifying a test report log for a regression test. The method further includes identifying one or more errors in the identified test report log. The method further includes determining a severity category for the one or more identified errors in the identified test report log. The method further includes determining a severity category for the identified test report log based on the determined severity category for the one or more identified errors in the identified test report log.