Abstract: A method for automatically modeling a codebase to facilitate management and analytics is provided. The method includes automatically aggregating a data set, the data set including data that correspond to a codebase; identifying, from the data set, entities and corresponding attributes, the entities relating to a component of the codebase; establishing, by using the data set, a relationship for the entities, the relationship corresponding to an interaction between each of the entities; determining a potential owner for each of the entities based on a corresponding change history; defining, by using the data set, an ownership rule for each of the entities, the ownership rule associating the potential owner to the corresponding entities; and computing an actionable item for each of the entities based on the corresponding attributes.

Abstract: A method and a system for applying a machine learning model that uses an artificial intelligence technique to determine that a software code commit is risky and therefore likely to result in a production issue and to provide an explanation for the risk are provided. The method includes: receiving a first code commit; analyzing the first code commit in order to determine features that relate to the first code commit; applying a machine learning model that uses an artificial intelligence technique to project a result of executing the first code commit; assessing whether the first code commit is risky based on an output of the machine learning model; and when the first code commit is assessed as being risky, determining an explanation that relates to the assessment.

Abstract: A method for automatically modeling a codebase to facilitate management and analytics is provided. The method includes automatically aggregating a data set, the data set including data that correspond to a codebase; identifying, from the data set, entities and corresponding attributes, the entities relating to a component of the codebase; establishing, by using the data set, a relationship for the entities, the relationship corresponding to an interaction between each of the entities; determining a potential owner for each of the entities based on a corresponding change history; defining, by using the data set, an ownership rule for each of the entities, the ownership rule associating the potential owner to the corresponding entities; and computing an actionable item for each of the entities based on the corresponding attributes.