Kyndryl, Inc.

Veeresh Bushetti

Nalini M

Kalpesh Sharma

Muniyandi Perumal Thevar

Prasanna Holla

Viswanatha Krishnamurthy

A computer-implemented method, according to one embodiment, includes causing a trained artificial intelligence (AI) model to derive data access privilege grants before data is stored in a predetermined database, and segmenting the data according to the data access privilege grants during storage of the data in the predetermined database. Metadata that defines the data access privilege grants is stored with the segments of data. The method further includes receiving, from a first user device, a query requesting data stored in the predetermined database. At least some of the metadata is read to identify a segment of the data associated with a data access privilege grant associated with the query. The method further includes allowing the first user device to access the identified segment of data for fulfilling the query.

Segmenting data according to data access privilege grants during storage of the data in a database

A computer-implemented method, in accordance with one embodiment, includes collecting data relating to development of a software product, the collected data including a plurality of different types of data relating to the development of the software product. A portion of the collected data is selected based on a characteristic of an intended user, the portion of the collected data including a plurality of the types of data. The selected portion of the collected data is transformed into data visualizations representing the data, the different types of the data having different data visualizations relative to one another. The data visualizations are output in a single visual representation for display to the intended user.

Generation of data visualizations on a single visual representation

A computer-implemented method, in accordance with one aspect of the present invention, includes collecting data existing in a development system that relates to the code change request, in response to receiving a code change request to merge new code with existing code. Factors from the collected data are computed for assessing a risk of promoting the new code, the factors including at least: a developer information factor, a developer availability factor, and an environment health analysis factor. The factors are processed to compute a confidence score for the code change request. If the confidence score is in a first predefined range, the new code corresponding to the code change request is promoted for merging with the existing code, without human intervention. If the confidence score is in a second predefined range, an indication that human intervention is needed before promoting the code is output.

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Savanur, India

Veeresh Bushetti