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: 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: 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 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.