Abstract: An approach for generating a modified software application by leveraging a customized advertisement between application owner and application vendor. The approach includes identifying a software application from a software application store repository and retrieving information from the software application store repository associated with the software application. The approach creates a requirement based on the retrieved information and identifying one or more vendor based on the requirement. After creating the targeted advertisement aimed at an application owner based on the identified vendor then the approach launches the advertisement campaign. The approach receives the notification from the application owner based on the advertisement to engage the identified vendor and modifies the software application based on the requirement.

Abstract: As part of identifying a theme corresponding to a wireframe, the wireframe comprising a set of graphical elements is analyzed, the set of graphical elements specifying a graphical representation of a user interface of a front-end application. A similarity measure is computed, the similarity measure quantifying a degree of similarity between the theme and an entry in a feature implementation history stored in a code repository. From the entry, a first feature to be implemented in a back-end application is extracted, the first feature servicing a data request from the front-end application. A source code stub extracted from the code repository, comprising a partial implementation of the first feature in the back-end application, is coupled with a first graphical element in the set of graphical elements in the wireframe.

Abstract: As part of identifying a theme corresponding to a wireframe, the wireframe comprising a set of graphical elements is analyzed, the set of graphical elements specifying a graphical representation of a user interface of a front-end application. A similarity measure is computed, the similarity measure quantifying a degree of similarity between the theme and an entry in a feature implementation history stored in a code repository. From the entry, a first feature to be implemented in a back-end application is extracted, the first feature servicing a data request from the front-end application. A source code stub extracted from the code repository, comprising a partial implementation of the first feature in the back-end application, is coupled with a first graphical element in the set of graphical elements in the wireframe.

Abstract: As part of identifying a theme corresponding to a wireframe, the wireframe comprising a set of graphical elements is analyzed, the set of graphical elements specifying a graphical representation of a user interface of a front-end application. A similarity measure is computed, the similarity measure quantifying a degree of similarity between the theme and an entry in a feature implementation history stored in a code repository. From the entry, a first feature to be implemented in a back-end application is extracted, the first feature servicing a data request from the front-end application. A source code stub extracted from the code repository, comprising a partial implementation of the first feature in the back-end application, is coupled with a first graphical element in the set of graphical elements in the wireframe.

Abstract: As part of identifying a theme corresponding to a wireframe, the wireframe comprising a set of graphical elements is analyzed, the set of graphical elements specifying a graphical representation of a user interface of a front-end application. A similarity measure is computed, the similarity measure quantifying a degree of similarity between the theme and an entry in a feature implementation history stored in a code repository. From the entry, a first feature to be implemented in a back-end application is extracted, the first feature servicing a data request from the front-end application. A source code stub extracted from the code repository, comprising a partial implementation of the first feature in the back-end application, is coupled with a first graphical element in the set of graphical elements in the wireframe.

Abstract: An approach for generating a modified software application by leveraging a customized advertisement between application owner and application vendor. The approach includes identifying a software application from a software application store repository and retrieving information from the software application store repository associated with the software application. The approach creates a requirement based on the retrieved information and identifying one or more vendor based on the requirement. After creating the targeted advertisement aimed at an application owner based on the identified vendor then the approach launches the advertisement campaign. The approach receives the notification from the application owner based on the advertisement to engage the identified vendor and modifies the software application based on the requirement.

Abstract: A method, computer program product, and a system where a processor(s) monitors activities of a user operating a vehicle with a navigational device providing route guidance, via a first route, to a geographic destination. The processor(s) identifies driving patterns of the user to generate a driver profile for the user, including a baseline activity pattern. The processor(s) determines, at a given time, that the monitored activities of the user are outside of the baseline activity pattern and enables an integrated image capture device in the vehicle and captures images of an environment outside of a the vehicle. The processor(s) utilizes the image capture device, to capture images and derives data describing environmental conditions, based on performing a semantic analysis of the images. The processor(s) transmits the data to a repository and generates a second route to the destination, based on the driver profile and relevant data from the repository data.

Abstract: Runtime system statistics logging is dynamically controlled at code and application levels, based on user reviews. Logging of specific code components in specific application instances, identified based on user reviews, is automatically turned on, based on the user reviews indicating defects. Logging for other components or application instances, however, remains off or is automatically turned off.

Abstract: Runtime system statistics logging is dynamically controlled at code and application levels, based on user reviews. Logging of specific code components in specific application instances, identified based on user reviews, is automatically turned on, based on the user reviews indicating defects. Logging for other components or application instances, however, remains off or is automatically turned off.

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 method, computer program product, and a system where a processor(s) monitors activities of a user operating a vehicle with a navigational device providing route guidance, via a first route, to a geographic destination. The processor(s) identifies driving patterns of the user to generate a driver profile for the user, including a baseline activity pattern. The processor(s) determines, at a given time, that the monitored activities of the user are outside of the baseline activity pattern and enables an integrated image capture device in the vehicle and captures images of an environment outside of a the vehicle. The processor(s) utilizes the image capture device, to capture images and derives data describing environmental conditions, based on performing a semantic analysis of the images. The processor(s) transmits the data to a repository and generates a second route to the destination, based on the driver profile and relevant data from the repository data.

Abstract: Runtime system statistics logging is dynamically controlled at code and application levels, based on user reviews. Logging of specific code components in specific application instances, identified based on user reviews, is automatically turned on, based on the user reviews indicating defects. Logging for other components or application instances, however, remains off or is automatically turned off.

Abstract: Runtime system statistics logging is dynamically controlled at code and application levels, based on user reviews. Logging of specific code components in specific application instances, identified based on user reviews, is automatically turned on, based on the user reviews indicating defects. Logging for other components or application instances, however, remains off or is automatically turned off.

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: 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.