Accelerating software change requests in deployment pipelines
Systems, machine-readable media, and methods may facilitate accelerating software change requests in deployment pipelines. Operations of a deployment pipeline for testing and deploying a software change may be monitored, the operations corresponding to a set of input requirements for testing and deploying the software change. Data composites may be collected. The data composites may be created and may include pipeline information associated with operations of the deployment pipeline. Rules corresponding to software change request protocols may be learned and may define mappings of the data composites to requirements pertaining to software change request operations of the deployment pipeline. Input required for a software change request may be decreased based on the learning so that the input required for the software change request conforms to a subset of the set of input requirements.
1. A system comprising:
one or more processing devices; and
memory communicatively coupled with and readable by the one or more processing devices and having stored therein processor-readable instructions which, when executed by the one or more processing devices, cause the one or more processing devices to perform operations comprising:
monitoring operations of a deployment pipeline for testing and deploying a software change to a production computing service, where the operations correspond at least in part to a set of input requirements for the testing and the deploying of the software change;
based at least in part on the monitoring, collecting data composites in a data storage, where:
each data composite comprises pipeline information associated with one or more operations of the deployment pipeline; and
the collecting comprises:
receiving the pipeline information via a network interface;
creating the data composites, where the data composites comprise the pipeline information associated with the one or more operations of the deployment pipeline; and
storing the data composites in the data storage;
based at least in part on the data composites, learning rules corresponding to one or more software change request protocols, the rules defining mappings of the data composites to requirements pertaining to software change request operations of the deployment pipeline; and
decreasing input required for a software change request based at least in part on the learning so that the input required for the software change request conforms to a subset of the set of input requirements.
2. The system as recited in claim 1 , where the decreasing the input comprises selecting a subset of input attributes from a set of input attributes, the set of input attributes specifying data fields pertaining to the software change request, and the subset of input attributes specifying a subset of the data fields.
3. The system as recited in claim 2 , the operations further comprising:
based at least in part on the subset of input attributes and the rules corresponding to one or more software change request protocols, generating a set of objects to facilitate setup of the software change request, the set of objects corresponding at least in part to the data composites.
4. The system as recited in claim 3 , the operations further comprising:
processing input received via a user interface of the deployment pipeline, the received input conforming to the subset of the set of input requirements.
5. The system as recited in claim 4 , the operations further comprising:
storing the set of objects in association with the software change request; and
populating the set of objects at least in part with the data composites.
6. The system as recited in claim 5 , the operations further comprising:
configuring the software change request in accordance with a software change request protocol, using the populated set of objects.
7. The system as recited in claim 1 , where the collecting further comprises analyzing the operations corresponding to testing and/or deploying one or more software changes to identify the pipeline information associated with one or more operations of the deployment pipeline.
8. One or more non-transitory, machine-readable media having machine-readable instructions thereon which, when executed by one or more processing devices, cause the one or more processing devices to perform operations comprising:
monitoring operations of a deployment pipeline for testing and deploying a software change to a production computing service, where the operations correspond at least in part to a set of input requirements for the testing and the deploying of the software change;
based at least in part on the monitoring, collecting data composites in a data storage, where:
each data composite comprises pipeline information associated with one or more operations of the deployment pipeline; and
the collecting comprises:
receiving the pipeline information via a network interface;
creating the data composites, where the data composites comprise the pipeline information associated with the one or more operations of the deployment pipeline; and
storing the data composites in the data storage;
based at least in part on the data composites, learning rules corresponding to one or more software change request protocols, the rules defining mappings of the data composites to requirements pertaining to software change request operations of the deployment pipeline; and
decreasing input required for a software change request based at least in part on the learning so that the input required for the software change request conforms to a subset of the set of input requirements.
9. The one or more non-transitory, machine-readable media as recited in claim 8 , where the decreasing the input comprises selecting a subset of input attributes from a set of input attributes, the set of input attributes specifying data fields pertaining to the software change request, and the subset of input attributes specifying a subset of the data fields.
10. The one or more non-transitory, machine-readable media as recited in claim 9 , the operations further comprising:
based at least in part on the subset of input attributes and the rules corresponding to one or more software change request protocols, generating a set of objects to facilitate setup of the software change request, the set of objects corresponding at least in part to the data composites.
11. The one or more non-transitory, machine-readable media as recited in claim 10 , the operations further comprising:
processing input received via a user interface of the deployment pipeline, the received input conforming to the subset of the set of input requirements.
12. The one or more non-transitory, machine-readable media as recited in claim 11 , the operations further comprising:
storing the set of objects in association with the software change request; and
populating the set of objects at least in part with the data composites.
13. The one or more non-transitory, machine-readable media as recited in claim 12 , the operations further comprising:
configuring the software change request in accordance with a software change request protocol, using the populated set of objects.
14. The one or more non-transitory, machine-readable media as recited in claim 8 , where the collecting further comprises analyzing the operations corresponding to testing and/or deploying one or more software changes to identify the pipeline information associated with one or more operations of the deployment pipeline.
15. A method comprising:
monitoring operations of a deployment pipeline for testing and deploying a software change to a production computing service, where the operations correspond at least in part to a set of input requirements for the testing and the deploying of the software change;
based at least in part on the monitoring, collecting data composites in a data storage, where:
each data composite comprises pipeline information associated with one or more operations of the deployment pipeline; and
the collecting comprises:
receiving the pipeline information via a network interface;
creating the data composites, where the data composites comprise the pipeline information associated with the one or more operations of the deployment pipeline; and
storing the data composites in the data storage;
based at least in part on the data composites, learning rules corresponding to one or more software change request protocols, the rules defining mappings of the data composites to requirements pertaining to software change request operations of the deployment pipeline; and
decreasing input required for a software change request based at least in part on the learning so that the input required for the software change request conforms to a subset of the set of input requirements.
16. The method as recited in claim 15 , where the decreasing the input comprises selecting a subset of input attributes from a set of input attributes, the set of input attributes specifying data fields pertaining to the software change request, and the subset of input attributes specifying a subset of the data fields.
17. The method as recited in claim 16 , further comprising:
based at least in part on the subset of input attributes and the rules corresponding to one or more software change request protocols, generating a set of objects to facilitate setup of the software change request, the set of objects corresponding at least in pan to the data composites.
18. The method as recited in claim 17 , further comprising:
processing input received via a user interface of the deployment pipeline, the received input conforming to the subset of the set of input requirements.
19. The method as recited in claim 18 , further comprising:
storing the set of objects in association with the software change request; and
populating the set of objects at least in part with the data composites.
20. The method as recited in claim 19 , further comprising:
configuring the software change request in accordance with a software change request protocol, using the populated set of objects.