Coordinated source code commits utilizing risk and error tolerance
A processor may receive data regarding a reference source code commit. The processor may identify, using an artificial intelligence model, a first group of source code commits including source code commits similar to the reference source code commit, where each source code commit in the first group is associated with a repository. The processor may determine, using the artificial intelligence model, a first risk associated with implementing a first source code commit from the first group of source code commits. The processor may determine an error budget associated with a first repository associated with the first source code commit. The processor may determine a first time for implementing the first source code commit.
1. A computer-implemented method, the method comprising:
receiving, by a processor of a computer device, data regarding a reference source code commit;
identifying, by an artificial intelligence model executed by the processor, a first group of source code commits including source code commits similar to the reference source code commit, wherein each source code commit in the first group is associated with a repository;
determining, by the artificial intelligence model, a first risk associated with implementing a first source code commit from the first group of source code commits;
determining, by the processor, an error budget associated with a first repository associated with the first source code commit; and
determining, by the processor, a first time for implementing the first source code commit based on the first risk and the error budget.
2. The method of claim 1 , further comprising:
identifying that a threshold time period has passed since the first time; and
determining whether an error associated with the first repository occurred during the threshold time period.
3. The method of claim 2 , further comprising:
generating a revised first risk associated with implementing the first source code commit.
4. The method of claim 2 , further comprising:
updating the error budget associated with the first repository based on an occurrence of the error.
5. The method of claim 2 , further comprising:
determining, based on the occurrence of the error, a revised implementation time for implementing a second source code commit from the first group.
6. The method of claim 1 , further comprising:
receiving, by the processor, a second reference source code commit;
identifying, using the artificial intelligence model, a second group of source code commits including source code commits similar to the second reference source code commit, wherein each source code commit in the second group is associated with a repository;
determining, using the artificial intelligence model, a second risk associated with implementing a particular source code commit of the second group, wherein the particular source code commit is associated with the first repository; and
determining a particular time for implementing the particular source code commit of the second group based on a comparison of the second risk to the first risk.
7. The method of claim 6 , further comprising:
identifying that a threshold time period has passed since the particular time; and
determining whether an error associated with the first repository occurred during the threshold time period.
8. A system comprising:
a memory; and
a processor in communication with the memory, the processor being configured to perform operations comprising:
receiving data regarding a reference source code commit;
identifying, using an artificial intelligence model, a first group of source code commits including source code commits similar to the reference source code commit, wherein each source code commit in the first group is associated with a repository;
determining, using the artificial intelligence model, a first risk associated with implementing a first source code commit from the first group of source code commits;
determining an error budget associated with a first repository associated with the first source code commit; and
determining a first time for implementing the first source code commit based on the first risk and the error budget.
9. The system of claim 8 , the processor being further configured to perform operations comprising:
identifying that a threshold time period has passed since the first time; and
determining whether an error associated with the first repository occurred during the threshold time period.
10. The system of claim 9 , the processor being further configured to perform operations comprising:
generating a revised first risk associated with implementing the first source code commit.
11. The system of claim 9 , the processor being further configured to perform operations comprising:
updating the error budget associated with the first repository based on an occurrence of the error.
12. The system of claim 9 , the processor being further configured to perform operations comprising:
determining, based on the occurrence of the error, a revised implementation time for implementing a second source code commit from the first group.
13. The system of claim 8 , the processor being further configured to perform operations comprising:
receiving a second reference source code commit;
identifying, using the artificial intelligence model, a second group of source code commits including source code commits similar to the second reference source code commit, wherein each source code commit in the second group is associated with a repository;
determining, using the artificial intelligence model, a second risk associated with implementing a particular source code commit of the second group, wherein the particular source code commit is associated with the first repository; and
determining a particular time for implementing the particular source code commit of the second group based on a comparison of the second risk to the first risk.
14. The system of claim 13 , the processor being further configured to perform operations comprising:
identifying that a threshold time period has passed since the particular time; and
determining whether an error associated with the first repository occurred during the threshold time period.
15. A computer program product comprising a computer readable storage medium storing instructions that when executed by a processor cause the processor to perform:
receiving data regarding a reference source code commit;
identifying, using an artificial intelligence model, a first group of source code commits including source code commits similar to the reference source code commit, wherein each source code commit in the first group is associated with a repository;
determining, using the artificial intelligence model, a first risk associated with implementing a first source code commit from the first group of source code commits;
determining an error budget associated with a first repository associated with the first source code commit; and
determining a first time for implementing the first source code commit based on the first risk and the error budget.
16. The computer program product of claim 15 , where the instructions further cause the processor to perform:
identifying that a threshold time period has passed since the first time; and
determining whether an error associated with the first repository occurred during the threshold time period.
17. The computer program product of claim 16 , where the instructions further cause the processor to perform:
generating a revised first risk associated with implementing the first source code commit.
18. The computer program product of claim 16 , where the instructions further cause the processor to perform:
updating the error budget associated with the first repository based on an occurrence of the error.
19. The computer program product of claim 16 , where the instructions further cause the processor to perform:
determining, based on the occurrence of the error, a revised implementation time for implementing a second source code commit from the first group.
20. The computer program product of claim 15 , where the instructions further cause the processor to perform:
receiving a second reference source code commit;
identifying, using the artificial intelligence model, a second group of source code commits including source code commits similar to the second reference source code commit, wherein each source code commit in the second group is associated with a repository;
determining, using the artificial intelligence model, a second risk associated with implementing a particular source code commit of the second group, wherein the particular source code commit is associated with the first repository; and
determining a particular time for implementing the particular source code commit of the second group based on a comparison of the second risk to the first risk.