Abstract: A method for resolving breaking changes in a software system. The method includes generating a training dataset of training examples by: collecting data associated with test results of tests configured to identify breaking changes in the software system and executed management actions; storing the collected test results data; storing the collected management action data; parsing the stored content and identifying failed test data and management action data relevant to a given failed test for identification as a training example. The method continues by training a machine learning model using the training examples included in the training dataset and then using the trained machine learning model to predict recommended management actions by: detecting a present failed test; determining failed test data; providing the failed test data to the trained machine learning model for generating therewith the recommended management actions.

Abstract: A method for resolving breaking changes in a software system. The method includes generating a training dataset of training examples by: collecting data associated with test results of tests configured to identify breaking changes in the software system and executed management actions; storing the collected test results data; storing the collected management action data; parsing the stored content and identifying failed test data and management action data relevant to a given failed test for identification as a training example. The method continues by training a machine learning model using the training examples included in the training dataset and then using the trained machine learning model to predict recommended management actions by: detecting a present failed test; determining failed test data; providing the failed test data to the trained machine learning model for generating therewith the recommended management actions.