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: 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: 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: 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.

Abstract: Approaches presented herein enable identification of an owner of a misplaced item. More specifically, an owner identification system receives information about an item from a finder of the item and generates, based on the information, a found item profile including characteristics historically associated with typical owners of such an item. The system generates a set of profiles of user preferences based on social media activity of the users and determines, based on a comparison of the found item profile with the user preferences profile, a likelihood that the user is the owner of the item. Based on this determination, the finder of the item can be notified of an identification of a potential owner. Successful matches between found items and their owners can be entered into a cognitive learning system to improve future outcomes.

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: 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 method for creating agnostic test automation script commands associated with a specified action and a user interface (UI) is provided. The method may include receiving a target element associated with the specified action. The method may also generate at least one anchor uniquely identifying the target element, wherein the target element is ambiguous. The method may further include generating an agnostic test automation script command based on the received target element and the at least one generated anchor. Additionally, the method may analyze, using the generated agnostic test automation script command, the at least one generated anchor to locate the target element within the user interface (UI), wherein the analyzing is based on an application on which the agnostic test automation script command is to be executed. The method may also include performing, using the generated agnostic test automation script command, the specified action on the located target element.

Abstract: A method for creating agnostic test automation script commands associated with a specified action and a user interface (UI) is provided. The method may include receiving a target element associated with the specified action. The method may also generate at least one anchor uniquely identifying the target element, wherein the target element is ambiguous. The method may further include generating an agnostic test automation script command based on the received target element and the at least one generated anchor. Additionally, the method may analyze, using the generated agnostic test automation script command, the at least one generated anchor to locate the target element within the user interface (UI), wherein the analyzing is based on an application on which the agnostic test automation script command is to be executed. The method may also include performing, using the generated agnostic test automation script command, the specified action on the located target element.

Abstract: A method for creating agnostic test automation script commands associated with a specified action and a user interface (UI) is provided. The method may include receiving a target element associated with the specified action. The method may also generate at least one anchor uniquely identifying the target element, wherein the target element is ambiguous. The method may further include generating an agnostic test automation script command based on the received target element and the at least one generated anchor. Additionally, the method may analyze, using the generated agnostic test automation script command, the at least one generated anchor to locate the target element within the user interface (UI), wherein the analyzing is based on an application on which the agnostic test automation script command is to be executed. The method may also include performing, using the generated agnostic test automation script command, the specified action on the located target element.

Abstract: Problem diagnostics may be obtained from components that log messages using a unique component identifier which is provided within each message so as to enable routing of callbacks to the component that originated the message. Similarly, problem diagnostics may be obtained from components that generate alerts, where the alerts contain the unique identifier of the component that generated the alert. Each component supports a diagnostic provider interface comprising operations which may be dynamically invoked, for example to solicit information from the component such as its configuration data, its state information, to execute self-diagnostic tests, and so forth. In addition or instead, operations may be provided that can be invoked to cause the component to alter its configuration data (such as notifying the component to change its level of message logging) or its behavior.