Abstract: A computer-implemented facility is provided for intelligent mobile device selection for mobile application testing. The computer-implemented facility determines features of a new mobile application to be tested, and compares the features of the new mobile application with features of multiple known mobile applications to identify one or more known mobile applications with similar features. Based at least in part on automated analysis of user reviews of the one or the more known mobile applications operating in one or more types of mobile devices, the facility provides one or more risk scores for operation of the new mobile application in the one or more types of mobile devices. Further, based on the risk scores, a recommended set of mobile devices in which to test the new mobile application may be generated for use in testing the new mobile application.

Abstract: Disclosed aspects relate to debugging a set of code components of an application program. A set of defect data which indicates a set of defects may be collected with respect to an application program. The set of defect data may be derived from a set of post-compilation users of the application program. A set of test case data which indicates a set of user interface features of the application program may be collected with respect to the application program. The set of test case data may be derived from a set of development tests of the application program. Using both the set of defect data and the set of test case data, a set of fragility data for the set of code components of the application program may be determined. Based on the set of fragility data, the set of code components of the application program may be debugged.

Abstract: Disclosed aspects relate to debugging a set of code components of an application program. A set of defect data which indicates a set of defects may be collected with respect to an application program. The set of defect data may be derived from a set of post-compilation users of the application program. A set of test case data which indicates a set of user interface features of the application program may be collected with respect to the application program. The set of test case data may be derived from a set of development tests of the application program. Using both the set of defect data and the set of test case data, a set of fragility data for the set of code components of the application program may be determined. Based on the set of fragility data, the set of code components of the application program may be debugged.

Abstract: An approach is provided for auto-adjusting instructional video playback based on cognitive user activity detection analysis. The approach includes, for instance, providing for playback an instructional video, including a series a steps to accomplish one or more tasks, and monitoring, during playback of the instructional video, progress of a user through the series of steps. The monitoring includes, at least in part, video monitoring the user, and using an activity detection analysis to detect, based on the monitoring, actions by the user as the user progresses through the series of steps. Playback of the instructional video is automatically adjusted based on the activity detection analysis to match the progress of the user through the series of steps.

Abstract: Embodiments of the invention provide for malware collusion detection in a mobile computing device. In one embodiment, a method for malicious inter-application interaction detection in a mobile computing device includes filtering applications installed in a mobile device to a set of related applications and then monitoring in the mobile device execution of the related applications in the set. The method additionally includes computing resource utilization of one of the related applications executing in a background of the mobile device while also computing execution performance of a different one of the related applications. Finally, the method includes responding to a determination that the computed resource utilization is high while the computed execution performance is poor by generating a notification in the display of the mobile device that the one of the related applications is suspected of malware collusion with the different one of the related applications.

Abstract: Disclosed aspects relate to debugging a set of code components of an application program. A set of defect data which indicates a set of defects may be collected with respect to an application program. The set of defect data may be derived from a set of post-compilation users of the application program. A set of test case data which indicates a set of user interface features of the application program may be collected with respect to the application program. The set of test case data may be derived from a set of development tests of the application program. Using both the set of defect data and the set of test case data, a set of fragility data for the set of code components of the application program may be determined. Based on the set of fragility data, the set of code components of the application program may be debugged.

Abstract: Disclosed aspects relate to debugging a set of code components of an application program. A set of defect data which indicates a set of defects may be collected with respect to an application program. The set of defect data may be derived from a set of post-compilation users of the application program. A set of test case data which indicates a set of user interface features of the application program may be collected with respect to the application program. The set of test case data may be derived from a set of development tests of the application program. Using both the set of defect data and the set of test case data, a set of fragility data for the set of code components of the application program may be determined. Based on the set of fragility data, the set of code components of the application program may be debugged.

Abstract: A computer-implemented facility is provided for intelligent mobile device selection for mobile application testing. The computer-implemented facility determines features of a new mobile application to be tested, and compares the features of the new mobile application with features of multiple known mobile applications to identify one or more known mobile applications with similar features. Based at least in part on automated analysis of user reviews of the one or the more known mobile applications operating in one or more types of mobile devices, the facility provides one or more risk scores for operation of the new mobile application in the one or more types of mobile devices. Further, based on the risk scores, a recommended set of mobile devices in which to test the new mobile application may be generated for use in testing the new mobile application.

Abstract: A computer-implemented facility is provided for intelligent mobile device selection for mobile application testing. The computer-implemented facility determines features of a new mobile application to be tested, and compares the features of the new mobile application with features of multiple known mobile applications to identify one or more known mobile applications with similar features. Based at least in part on automated analysis of user reviews of the one or the more known mobile applications operating in one or more types of mobile devices, the facility provides one or more risk scores for operation of the new mobile application in the one or more types of mobile devices. Further, based on the risk scores, a recommended set of mobile devices in which to test the new mobile application may be generated for use in testing the new mobile application.

Abstract: An approach is provided for auto-adjusting instructional video playback based on cognitive user activity detection analysis. The approach includes, for instance, providing for playback an instructional video, including a series a steps to accomplish one or more tasks, and monitoring, during playback of the instructional video, progress of a user through the series of steps. The monitoring includes, at least in part, video monitoring the user, and using an activity detection analysis to detect, based on the monitoring, actions by the user as the user progresses through the series of steps. Playback of the instructional video is automatically adjusted based on the activity detection analysis to match the progress of the user through the series of steps.

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: Embodiments of the present disclosure provide a method, system, and computer program product for collecting serviceability data for analyzing a customer's issue. An analysis agent is launched upon initiation of a trace as a result of an error. The analysis agent accesses trace specification on a customer environment to determine an order of processing for a specific task. The analysis agent maps the trace specification to a set of APIs invoked by an operating system for the specific task. The analysis recreates the error in the customer environment based on the mapping of the trace specification to the set of APIs invoked by the operating system. The analysis agent diagnoses the error while monitoring the recreation of the error in the customer environment.

Abstract: A computer-implemented facility is provided for intelligent mobile device selection for mobile application testing. The computer-implemented facility determines features of a new mobile application to be tested, and compares the features of the new mobile application with features of multiple known mobile applications to identify one or more known mobile applications with similar features. Based at least in part on automated analysis of user reviews of the one or the more known mobile applications operating in one or more types of mobile devices, the facility provides one or more risk scores for operation of the new mobile application in the one or more types of mobile devices. Further, based on the risk scores, a recommended set of mobile devices in which to test the new mobile application may be generated for use in testing the new mobile application.

Abstract: A computer-implemented facility is provided for intelligent mobile device selection for mobile application testing. The computer-implemented facility determines features of a new mobile application to be tested, and compares the features of the new mobile application with features of multiple known mobile applications to identify one or more known mobile applications with similar features. Based at least in part on automated analysis of user reviews of the one or the more known mobile applications operating in one or more types of mobile devices, the facility provides one or more risk scores for operation of the new mobile application in the one or more types of mobile devices. Further, based on the risk scores, a recommended set of mobile devices in which to test the new mobile application may be generated for use in testing the new mobile application.

Abstract: A computer-implemented facility is provided for intelligent mobile device selection for mobile application testing. The computer-implemented facility determines features of a new mobile application to be tested, and compares the features of the new mobile application with features of multiple known mobile applications to identify one or more known mobile applications with similar features. Based at least in part on automated analysis of user reviews of the one or the more known mobile applications operating in one or more types of mobile devices, the facility provides one or more risk scores for operation of the new mobile application in the one or more types of mobile devices. Further, based on the risk scores, a recommended set of mobile devices in which to test the new mobile application may be generated for use in testing the new mobile application.

Abstract: Embodiments of the invention provide for malware collusion detection in a mobile computing device. In one embodiment, a method for malicious inter-application interaction detection in a mobile computing device includes filtering applications installed in a mobile device to a set of related applications and then monitoring in the mobile device execution of the related applications in the set. The method additionally includes computing resource utilization of one of the related applications executing in a background of the mobile device while also computing execution performance of a different one of the related applications. Finally, the method includes responding to a determination that the computed resource utilization is high while the computed execution performance is poor by generating a notification in the display of the mobile device that the one of the related applications is suspected of malware collusion with the different one of the related applications.

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: Disclosed aspects relate to debugging a set of code components of an application program. A set of defect data which indicates a set of defects may be collected with respect to an application program. The set of defect data may be derived from a set of post-compilation users of the application program. A set of test case data which indicates a set of user interface features of the application program may be collected with respect to the application program. The set of test case data may be derived from a set of development tests of the application program. Using both the set of defect data and the set of test case data, a set of fragility data for the set of code components of the application program may be determined. Based on the set of fragility data, the set of code components of the application program may be debugged.

Abstract: Disclosed aspects relate to debugging a set of code components of an application program. A set of defect data which indicates a set of defects may be collected with respect to an application program. The set of defect data may be derived from a set of post-compilation users of the application program. A set of test case data which indicates a set of user interface features of the application program may be collected with respect to the application program. The set of test case data may be derived from a set of development tests of the application program. Using both the set of defect data and the set of test case data, a set of fragility data for the set of code components of the application program may be determined. Based on the set of fragility data, the set of code components of the application program may be debugged.

Abstract: Disclosed aspects relate to debugging a set of code components of an application program. A set of defect data which indicates a set of defects may be collected with respect to an application program. The set of defect data may be derived from a set of post-compilation users of the application program. A set of test case data which indicates a set of user interface features of the application program may be collected with respect to the application program. The set of test case data may be derived from a set of development tests of the application program. Using both the set of defect data and the set of test case data, a set of fragility data for the set of code components of the application program may be determined. Based on the set of fragility data, the set of code components of the application program may be debugged.