Abstract: A method for creating a learning model that evaluates risks of applying commits in a third-party product to a dependent product is disclosed. The method includes collecting data on past commits; training the learning model using the collected data; and using the learning model to determine if future commits are problematic.

Abstract: A method of determining if a commit is problematic includes determining a complexity of the commit; determining an author of the commit; determining an experience of the author; determining a component affected by the commit; and assigning a risk value to the commit based on the complexity, the author, the experience of the author and the component affected.

Abstract: A method for testing commits from a third-party product into a dependent product includes receiving a first commit from a third-party product; waiting for additional commits from the third-party product; receiving a second commit from the third-party product; testing the first and second commit using a pre-trained learning model; determining if the first commit is problematic, and if the first commit is problematic, sending the first commit for review before implementation; and determining if the second commit is problematic, and if the second commit is problematic, sending the second commit for review before implementation. Accumulating the first and second commits for testing at once reduces system resources.

Abstract: A method for alerting a community to potential problematic commits includes receiving a commit submitted by an author in a project; testing the commit using a pre-trained learning model; and determining if the commit is problematic, and if the commit is problematic, sending a report in the project outlining the level of risk of implementing the commit and a reason for the level or risk. Users of the project will be alerted to potential risk of particular commits.

Abstract: A method for creating a learning model that evaluates risks of applying commits in a third-party product to a dependent product is disclosed. The method includes collecting data on past commits; training the learning model using the collected data; and using the learning model to determine if future commits are problematic.