Software validation framework

An apparatus comprising:

• a processing device operatively coupled to a memory and configured to:
• receive a request for validation of software comprising one or more applications;
• analyze the request and generate one or more validation steps based at least in part on the analysis;
• and
• predict a time to complete the one or more validation steps, wherein:
• the predicting is performed using one or more machine learning models, and is based at least in part on a type and a number of the one or more applications;
• and
• in performing the predicting, the processing device is further configured to input a multi-dimensional feature vector comprising at least the type and the number of the one or more applications to the one or more machine learning models;
• wherein the one or more machine learning models are trained with at least a first dataset comprising data corresponding to times to complete previous software validations and corresponding to types and numbers of applications associated with the previous software validations;
• and
• wherein the one or more machine learning models are trained with at least a second dataset generated through a feedback loop and based at least in part on the predicted time to complete the one or more validation steps.

An article of manufacture comprising a non-transitory processor-readable storage medium having stored therein program code of one or more software programs, wherein the program code of the one or more software programs, when executed by at least one processing device, causes the at least one processing device to perform the steps of:

• receiving a request for validation of software comprising one or more applications;
• analyzing the request and generating one or more validation steps based at least in part on the analysis;
• and
• predicting a time to complete the one or more validation steps, wherein:
• the predicting is performed using one or more machine learning models, and is based at least in part on a type and a number of the one or more applications;
• and
• in performing the predicting, the program code of the one or more software programs further causes the at least one processing device to perform the step of inputting a multi-dimensional feature vector comprising at least the type and the number of the one or more applications to the one or more machine learning models;
• wherein the one or more machine learning models are trained with at least a first dataset comprising data corresponding to times to complete previous software validations and corresponding to types and numbers of applications associated with the previous software validations;
• and
• wherein the one or more machine learning models are trained with at least a second dataset generated through a feedback loop and based at least in part on the predicted time to complete the one or more validation steps.