DETERMINING IF A COMMIT IS PROBLEMATIC
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.
1 . A method of determining if a commit is problematic, the method comprising:
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.
2 . The method of claim 1 , further comprising if the risk value is high, sending the commit for review before implementation.
3 . The method of claim 2 , further comprising, if the risk is low, implementing the commit.
4 . The method of claim 3 , wherein implementing the future commit includes copying a pre-built version of the library rather than applying changes to an existing library.
5 . The method of claim 2 , further comprising sending the commit for review before implementation.
6 . The method of claim 5 , further comprising sending a report to a reviewer outlining a level of risk of implementing the commit.
7 . The method of claim 6 , further comprising sending a report that includes a reason for the level or risk.
8 . The method of claim 1 , wherein the complexity includes a number of characters in a commit message, a number of file changed, a number of code lines added and a number of code lines removed.
9 . The method of claim 1 , wherein the experience of the author includes a number of previous commits, a total number of code lines added, and a total number of code lines removed.
10 . The method of claim 1 , further including:
determining, by a pre-trained learning model, a complexity of the commit;
determining, by a pre-trained learning model, an author of the commit;
determining, by a pre-trained learning model, an experience of the author;
determining, by a pre-trained learning model, a component affected by the commit; and
assigning a risk value to the commit, by a pre-trained learning model, based on the complexity, the author, the experience of the author and the component affected.
11 . A non-transitory machine readable memory medium including instructions when executed to cause a processor to perform the following actions:
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.
12 . The non-transitory machine readable memory medium of claim 11 , further comprising if the risk value is high, sending the commit for review before implementation.
13 . The non-transitory machine readable memory medium of claim 12 , further comprising, if the risk is low, implementing the commit.
14 . The non-transitory machine readable memory medium of claim 13 , wherein implementing the future commit includes copying a pre-built version of the library rather than applying changes to an existing library.
15 . The non-transitory machine readable memory medium of claim 12 , further comprising sending the commit for review before implementation.
16 . The non-transitory machine readable memory medium of claim 15 , further comprising sending a report to a reviewer outlining a level of risk of implementing the commit.
17 . The non-transitory machine readable memory medium of claim 16 , further comprising sending a report that includes a reason for the level or risk.
18 . The non-transitory machine readable memory medium of claim 11 , wherein the complexity includes a number of characters in a commit message, a number of file changed, a number of code lines added and a number of code lines removed.
19 . The non-transitory machine readable memory medium of claim 11 , wherein the experience of the author includes a number of previous commits, a total number of code lines added, and a total number of code lines removed.
20 . The non-transitory machine readable memory medium of claim 11 , further including:
determining, by a pre-trained learning model, a complexity of the commit;
determining, by a pre-trained learning model, an author of the commit;
determining, by a pre-trained learning model, an experience of the author;
determining, by a pre-trained learning model, a component affected by the commit; and
assigning a risk value to the commit, by a pre-trained learning model, based on the complexity, the author, the experience of the author and the component affected.