Abstract: Example embodiments relate to generating sets of recommended inputs for changing predicted results of a predictive model. The examples disclosed herein access, from a database, a historical set of inputs and results of a predictive model. A function is approximated based on the historical set of inputs and results, and a gradient of the function is computed using a result of the function with respect to a local maximum value of the function. A set of recommended inputs is generated based on the gradient of the function, where a recommended input produces a positive result of the function.

Abstract: Application management based on data correlations is disclosed. One example is a system including a data processor, a data element generator, a matrix generator, a data analysis module, a performance module, and a load test manager. The data processor accesses test data based on an application under load testing. The data element generator generates a plurality of transactional data elements based on the test data, each data element comprising at least three data components. The matrix generator generates a covariance matrix based on the data components. The data analysis module determines an eigenvector associated with the covariance matrix, and identifies a correlation between a sub-plurality of the at least three data components based on coefficients of the eigenvector. The performance module determines, based on the correlation, performance metrics for the application under load testing. The load test manager manages, based on the performance metrics, the application under load testing.

Abstract: Example embodiments relate to generating sets of recommended inputs for changing predicted results of a predictive model. The examples disclosed herein access, from a database, a historical set of inputs and results of a predictive model. A function is approximated based on the historical set of inputs and results, and a gradient of the function is computed using a result of the function with respect to a local maximum value of the function. A set of recommended inputs is generated based on the gradient of the function, where a recommended input produces a positive result of the function.

Abstract: According to an example, user interface (UI) behavior based rules generation may include ascertaining data related to an application UI for a specified version of an application, and ascertaining context elements included in the data related to the application UI. UI behavior based rules generation may include ascertaining values associated with the context elements, and generating context combinations based on the context elements and the values associated with the context elements. UI behavior based rules generation may include determining a truth table for the application UI based on an analysis of fields of the application UI and corresponding context combinations, and generating, based on an analysis of the truth table, a rule that identifies customization of the specified version of the application.

Abstract: A risk level of a software commit is assessed through the use of a classifier. The classifier may be generated based on attributes pertaining to previous commits and used to determine a risk level for deployment of a software commit into a production environment based on attributes extracted from the software commit.

Abstract: In one example of the disclosure, a subject message for a display caused by a subject software application is obtained. A prediction model is utilized to identify the subject message as a first type message or a second type message. The model is a model determined based upon a set of target words determined by imposition of a set of rules upon a set of user facing messages extracted from a set of software applications, wherein each of the extracted messages was classified post-extraction as a first type message or a second type message. A communication identifying the subject message as the first type message or the second type message is provided.

Abstract: Application management based on data correlations is disclosed. One example is a system including a data processor, a data element generator, a matrix generator, a data analysis module, a performance module, and a load test manager. The data processor accesses test data based on an application under load testing. The data element generator generates a plurality of transactional data elements based on the test data, each data element comprising at least three data components. The matrix generator generates a covariance matrix based on the data components. The data analysis module determines an eigenvector associated with the covariance matrix, and identifies a correlation between a sub-plurality of the at least three data components based on coefficients of the eigenvector. The performance module determines, based on the correlation, performance metrics for the application under load testing. The load test manager manages, based on the performance metrics, the application under load testing.

Abstract: In one example in accordance with the present disclosure, a method for test session similarity determination includes capturing a sequence of events from a user session of an application and converting the captured sequence into a data format used for a test sequence. The method also includes converting each event in the test sequence that is not in the captured sequence into a disparate event and creating a unique set including each unique event in the captured sequence and the disparate event. The method also includes determining a first average relative location of the event in the captured sequence and a second average relative location of each event in the rest sequence. The method also includes determining a degree of similarity between the captured sequence and the test sequence based on a comparison of the first and second average relative location and automatically generating a visualization highlighting the degree of similarity.

Abstract: Examples disclosed herein enable identifying a feature that is common to a first dataset and a second dataset, wherein a first value of the feature in the first dataset is different from a second value of the feature in the second dataset; determining a first predicted value of the feature in the first dataset based on a second dataset classifier trained on the second dataset; determining a second predicted value of the feature in the second dataset based on a first dataset classifier trained on the first dataset; determining a first similarity score between the first value and the first predicted value; determining a second similarity score between the second value and the second predicted value; and generating a bipartite graph that comprises a first node indicating the first value, a second node indicating the second value, and an edge indicating the first or second similarity score.

Abstract: According to an example, a history of usage and a history of utilization of resources may be accessed. A usage regression model to predict a cloud resource usage and a utilization regression model to predict a cloud resource utilization at a future time period may be developed. The usage regression model and the utilization regression model may be used to manage cloud resource usage costs.