Abstract: Techniques are disclosed for predicting the probability of successfully building the software application whether an integration build between source components of a software application will be successful. An integration service executing on a server computer determines whether to test an integration build of source components of a software application. The integration service obtains metrics related to the developer of each component as well as any previous integration builds of the components. Based on the metrics, the integration service predicts a probability of a successful integration build of the source components. Based on the probability of a successful integration build, the integration service may notify a developer to perform an action to increase the likelihood of the integration build being successful.

Abstract: A computer-implemented method for acquiring benefits qualification data includes obtaining a profile corresponding to the individual. The method also includes forming respective sets of unanswered questions in each completeness graph in a set of completeness graphs by running the profile against each completeness graph. The method further includes forming a union set of unanswered questions in the set of completeness graphs from the respective sets of unanswered questions in each completeness graph. Moreover, the method includes identifying a high priority unanswered question in the union set of unanswered questions using a prioritization algorithm. Each completeness graph corresponds to a respective benefit program.

Abstract: The present disclosure relates to a system and method for evaluating source code. The system generates training data from a plurality of defect records that identify developers who fixed defects. The system recommends developers for repairing defects in software features using a machine learning algorithm configured using elements from the defect records and the code related to the defect records.

Abstract: An application determines an emotional state of a user based on evaluating facial recognition data of the user captured from the user interacting with the application. The application receives a request from the user to initiate a support call. The request identifies the emotional state of the user. The application predicts, from a set of outcomes of support calls processed by support agents interacting with users having different emotional states, an emotional state that increases a likelihood of achieving a specified outcome for the support call based on the emotional state of the user. The application identifies an available support agent having the predicted emotional state, and assigns the user to interact with the identified support agent for the support call.

Abstract: An application determines an emotional state of a user based on evaluating facial recognition data of the user captured from the user interacting with the application. The application receives a request from the user to initiate a support call. The request identifies the emotional state of the user. The application predicts, from a set of outcomes of support calls processed by support agents interacting with users having different emotional states, an emotional state that increases a likelihood of achieving a specified outcome for the support call based on the emotional state of the user. The application identifies an available support agent having the predicted emotional state, and assigns the user to interact with the identified support agent for the support call.

Abstract: An application determines an emotional state of a user based on evaluating facial recognition data of the user captured from the user interacting with the application. The application receives a request from the user to initiate a support call. The request identifies the emotional state of the user. The application predicts, from a set of outcomes of support calls processed by support agents interacting with users having different emotional states, an emotional state that increases a likelihood of achieving a specified outcome for the support call based on the emotional state of the user. The application identifies an available support agent having the predicted emotional state, and assigns the user to interact with the identified support agent for the support call.

Abstract: The present disclosure relates to dynamically generating user interfaces based on a user's emotional state. An example method generally includes a computer system receiving emotional response data from a client device. The computer system identifies a version of a user experience to present on the client device based on the received emotional response data and generates code for rendering a user interface associated with the identified version of the user experience. The generated code is transmitted to the client device for rendering and presentation on the client device.

Abstract: The present disclosure relates to live support integration in a virtual machine based development environment. According to one embodiment, a method generally includes obtaining, by a virtual machine in the virtual machine based development environment, a token from a secure location. In some embodiments, upon determining, that the token is authentic, the virtual machine determines system configuration information relating to the virtual machine. In certain embodiments, the virtual machine establishes a communication channel with a remote support device using the token. The establishing may comprise transmitting, by the virtual machine, the system configuration information to the remote support device and enabling two-way communication between the virtual machine and the remote support device.

Abstract: The present disclosure relates to error recovery in a virtual machine-based development environment. An example method generally includes monitoring for an error event in a virtual machine managed within a development environment. The development environment attempts to recover the virtual machine from the error event by performing a first error remediation procedure on the virtual machine. Upon determining that the first error remediation procedure failed to recover the virtual machine from the error event, the development environment attempts to recover the virtual machine from the error event by performing a second error remediation procedure on the virtual machine. The second remediation procedure may be a procedure that is more severe than the first error remediation procedure.

Abstract: The present disclosure relates to providing a virtual development environment by receiving a request for a development environment from a user, identifying resources from the request and resource on which the requested resource depends, and obtaining virtual machine images for each resource from a repository. The virtual machine images are executed in separate virtual machines to provide a development environment and related resources to the user.

Abstract: An application determines an emotional state of a user based on evaluating facial recognition data of the user captured from the user interacting with the application. The application receives a request from the user to initiate a support call. The request identifies the emotional state of the user. The application predicts, from a set of outcomes of support calls processed by support agents interacting with users having different emotional states, an emotional state that increases a likelihood of achieving a specified outcome for the support call based on the emotional state of the user. The application identifies an available support agent having the predicted emotional state, and assigns the user to interact with the identified support agent for the support call.

Abstract: The present disclosure relates to managing service dependencies in a virtual machine-based development environment. An example method generally includes retrieving information identifying one or more service dependencies for a first service hosted by a virtual machine being instantiated in a development environment. For each service dependency of the one or more service dependencies, a service dependency manager identifies whether the service dependency is accessible by the first service in the development environment. Upon determining that the service dependency is not accessible by the first service in the development environment, the service dependency manager spawns an instance of the service dependency and connects the service dependency to the virtual machine hosting the first service. If the service dependency is accessible by the first service in the development environment, the service dependency manager connects the service dependency to the virtual machine hosting the first service.

Abstract: An application determines an emotional state of a user based on evaluating facial recognition data of the user captured from the user interacting with the application. The application receives a request from the user to initiate a support call. The request identifies the emotional state of the user. The application predicts, from a set of outcomes of support calls processed by support agents interacting with users having different emotional states, an emotional state that increases a likelihood of achieving a specified outcome for the support call based on the emotional state of the user. The application identifies an available support agent having the predicted emotional state, and assigns the user to interact with the identified support agent for the support call.

Abstract: The present disclosure relates to a system and method for predicting errors in a software code base. The system generates an element set from a plurality of defect records that identify the developer who originated the defect, the code revisions related to the defect, and a classification of the defect. The system predicts the probability and type of defects in software using a machine learning algorithm configured using elements from the defect records and the code related to the defect records.

Abstract: An application determines an emotional state of a user based on evaluating facial recognition data of the user captured from the user interacting with the application. The application receives a request from the user to initiate a support call. The request identifies the emotional state of the user. The application predicts, from a set of outcomes of support calls processed by support agents interacting with users having different emotional states, an emotional state that increases a likelihood of achieving a specified outcome for the support call based on the emotional state of the user. The application identifies an available support agent having the predicted emotional state, and assigns the user to interact with the identified support agent for the support call.

Abstract: An application determines an emotional state of a user based on evaluating facial recognition data of the user captured from the user interacting with the application. The application receives a request from the user to initiate a support call. The request identifies the emotional state of the user. The application predicts, from a set of outcomes of support calls processed by support agents interacting with users having different emotional states, an emotional state that increases a likelihood of achieving a specified outcome for the support call based on the emotional state of the user. The application identifies an available support agent having the predicted emotional state, and assigns the user to interact with the identified support agent for the support call.

Abstract: An application infers emotional states of support agents from facial recognition data collected from support calls processed by the support agents. The application determines an outcome of each of the support calls based on feedback indicating user experience with the support agents. The application correlates outcomes of the support calls based on different topics with the emotional states. Upon receiving a request to initiate a support call, the application predicts, from the correlation, an emotional state that increases a likelihood of achieving a specified outcome for the support call based on a topic identified in the request. The application identifies an available support agent having the predicted emotional state, and assigns the identified support agent to interact with the user for the support call.

Abstract: A method to communicate driving status from a vehicle. The method includes obtaining, using a mobile device travelling in the vehicle, movement data of the vehicle, determining, using a computer processor of the mobile device, that the vehicle is in motion by analyzing the movement data based on a specified criterion, generating, in response to determining that the vehicle is in motion, driving status data of the vehicle by further analyzing the movement data based on the specified criterion, and transmitting, using the mobile device, the driving status data to a remote user away from the vehicle.

Abstract: A method to qualify a partner candidate, including accessing a social network to retrieve social network data about the partner candidate; parsing the social network data to extract a social network data item corresponding to a characteristics category relating to a characteristic of the partner candidate; determining per category weighting factors corresponding to characteristics categories and representing a focus of the social network; comparing, using a keyword library comprising pre-determined keywords and pre-determined keyword similarity measures, the social network data item and a self-described attribute of the partner candidate to generate a characteristics category score, wherein a range of the characteristics category score is determined by a corresponding per category weighting factor; and assigning the characteristics category score to the self-described attribute, wherein the characteristics category score represents a validity measure of the self-described attribute based on the social network

Abstract: In general, in one aspect, the invention relates to a method for maintaining a calendar of a user. The method includes: accessing an online profile of the user corresponding to a first social networking website; identifying, on the first social networking website, a first entry referencing a physical location, where the first entry is entered on a first calendar date; identifying, on the first social networking website, a second entry referencing the physical location, where the second entry is entered on a second calendar date; calculating, based on the first calendar date and the second calendar date, a first frequency of user visits to the physical location; estimating, based on the first frequency, a future calendar date when the user is expected to visit the physical location; and creating, in the calendar, a future event scheduled to occur at the physical location.