Abstract: A computer-implemented method assesses an impact of a change management strategy implementation. A first change management index score is identified. A graph theory is used to compute a degree centrality and a betweenness centrality of nodes, based on an adjacency matrix. A change management strategy framework is deployed based on the degree centrality, the betweenness centrality, and a closeness in a social media chain, to transition to one of the nodes having a probability above a predetermined probability threshold of influencing a change from one state to another. Personas are created that are associated with a particular technology, the identified first change management index score, the degree centrality, and the betweenness centrality of the nodes. A Markov chain model transition matrix measures a second change management index score after a time t is measured. A change management strategy framework is altered based on a parametric regression or a polynomial regression.

Abstract: Computer implemented methods, systems, and computer program products include program code executing on a processor(s) that ingests data from one or more computing environments, where the data is related to applications. The processor(s) identifies, based on utilizing topic modeling and latent semantic analysis of the data, homogenous applications among the applications, which include analyzing subdata handled by each application and functionalities of each application; the homogenous applications comprise similarities in the data and in the functionalities. The processor(s) determines overlapping data among the homogenous applications based on the topic modeling, the latent semantic analysis, and term frequency-inverse document frequency of terms in the overlapping data. The processor(s) selects, from the overlapping data, training data. The processor(s) utilizes the training data to calculate weights for disposition metrics and the metrics to predict the resource dispositions for the applications.

Abstract: In various examples, a computer-implemented method includes: ingesting, by one or more computing devices, application deployment data for an application; generating, by the one or more computing devices, a cloud application deployment predictor data structure for the application; generating, by the one or more computing devices, objective functions for the cloud application deployment predictor data structure for the application; optimizing, by the one or more computing devices, between the objective functions for the application; and generating, by the one or more computing devices, based on the optimizing between the objective functions, a multi-cloud deployment map for the application.

Abstract: An end-to-end framework that provides for pair programming can determine a project intent. Programming requirements needed to fulfill the project intent can be identified. The programming requirements can be reduced into lower dimensions. The reduced programming requirements can be clustered into clusters. A common theme can be identified in each of the clusters. From at least one of the clusters having a common theme corresponding to the programming requirements, feasible pairs of developers can be selected. At least one optimal pair of developers can be determined among the feasible pairs using an optimization algorithm that optimizes the project intent.

Abstract: Disclosed are techniques for training and utilizing a machine learning model for automatically accelerating technical incident triage. A historical dataset is combined from a plurality of sources corresponding to historical technical incidences and how they were resolved, including what persons or responders were involved in responding to the incident. This historical dataset is processed for input into a machine learning model which is trained to output a ranking of historical incidences based on similarity to an input incident. Machine logic then automatically selects a responder for the incident based on which available responders previously resolved the ranked historical incidences, selecting responders from the most similar historical incidences if they are available, and communicating information from the ranked historical incidences to assist in diagnosing and resolving the input incident.

Abstract: A method, system, and computer program product for implementing automated software application generation is provided. The method includes scanning source code for identifying missing elements of hardware and software parameters associated with functional operation of software for development. The hardware and software parameters are analyzed and converted into configurable digital bins. An automation parameter is generated. The automation parameter is associated with portions of the software configured for automatic development and generation. Corrective actions associated with automating development of the software are generated based on the automation parameter and it is determined that the automation parameter is within a specified range of the portions. The corrective actions are executed with respect to development of the software and the software is generated.

Abstract: A method includes receiving, by one or more processors of a computer system, historical data related to deployment characteristics and an architecture for past incidents occurring in a data warehouse, predicting, by the one or more processors of the computer system using neural network modeling, tickets related to a response to at least one incident occurring in the data warehouse, wherein the predicting is based on the deployment characteristics and the architecture of the data warehouse, considering, by the one or more processors of a computer system, a plurality of parameters to ascertain the predicted tickets, and providing, by the one or more processors of the computer system, incident ticket volume prediction including the predicted tickets to an incident management system interface reviewable by a user.

Abstract: A method, system, and computer program product for predicting optimal service characteristics to execute predicted IT tickets. The method may include identifying deployment characteristics for an operational system based on an architecture of the operational system. The method may also include receiving implementation data in response to past incident tickets based on the deployment characteristics. The method may also include identifying relationships within the implementation data. The method may also include predicting a volume of new tickets based on the implementation data and the relationships between the implementation data. The method may also include predicting a resolution time for high severity tickets in the volume of new tickets. The method may also include determining preferred service characteristics based on the volume of the new tickets and the resolution time for the high severity tickets.

Abstract: A method, system, and computer program product for implementing automated software application generation is provided. The method includes scanning source code for identifying missing elements of hardware and software parameters associated with functional operation of software for development. The hardware and software parameters are analyzed and converted into configurable digital bins. An automation parameter is generated. The automation parameter is associated with portions of the software configured for automatic development and generation. Corrective actions associated with automating development of the software are generated based on the automation parameter and it is determined that the automation parameter is within a specified range of the portions. The corrective actions are executed with respect to development of the software and the software is generated.

Abstract: A method, system, and computer program product for predicting optimal service characteristics to execute predicted IT tickets. The method may include identifying deployment characteristics for an operational system based on an architecture of the operational system. The method may also include receiving implementation data in response to past incident tickets based on the deployment characteristics. The method may also include identifying relationships within the implementation data. The method may also include predicting a volume of new tickets based on the implementation data and the relationships between the implementation data. The method may also include predicting a resolution time for high severity tickets in the volume of new tickets. The method may also include determining preferred service characteristics based on the volume of the new tickets and the resolution time for the high severity tickets.