Software update compatibility assessment
A method includes identifying at least one software update available for a given computing device, determining a state of the given computing device, and utilizing a machine-learning based predictive model to assess compatibility of the at least one software update with the given computing device based at least in part on the state of the given computing device, the machine learning-based predictive model being trained utilizing historical incident data for a plurality of incidents associated with application of software updates to a plurality of computing devices. The method also includes generating a recommendation notification indicating compatibility of the at least one software update with the given computing device, and providing the recommendation notification in conjunction with presentation of one or more user interface features controlling whether to apply the at least one software update to the given computing device.
1. An apparatus comprising: at least one processing device comprising a processor coupled to a memory; the at least one processing device being configured to perform steps of: identifying at least one software update available for a given computing device; determining a state of the given computing device; utilizing a machine learning-based predictive model to assess compatibility of the at least one software update with the given computing device based at least in part on the state of the given computing device, the machine learning-based predictive model being trained utilizing historical incident data for a plurality of incidents associated with application of software updates to a plurality of computing devices; generating a recommendation notification indicating compatibility of the at least one software update with the given computing device, wherein generating the recommendation notification comprises selecting a type of visual indicator based at least in part on the assessed compatibility indicating that the at least one software update has at least a designated threshold probability of affecting operation of the given computing device; and providing the recommendation notification in conjunction with presentation of one or more user interface features controlling whether to apply the at least one software update to the given computing device, wherein providing the recommendation notification in conjunction with presentation of the one or more user interface features controlling whether to apply the at least one software update to the given computing device comprises displaying the visual indicator; wherein the designated threshold probability is dynamically adjusted based at least in part on one or more characteristics of the at least one software update.
2. The apparatus of claim 1 wherein the at least one software update comprises at least one of: installing new software, upgrading existing software, patching existing software, removing existing software, and replacing existing software with different software.
3. The apparatus of claim 1 wherein the at least one software update is for at least one of an operating system of the given computing device, a system component of the given computing device, a device driver of the given computing device, and an application installed on the given computing device.
4. The apparatus of claim 1 wherein identifying the at least one software update available for the given computing device comprises receiving at least one software update notification from a software provider indicating that the at least one software update is available and compatible with a device model of the given computing device.
5. The apparatus of claim 1 wherein determining the state of the given computing device comprises collecting information regarding at least one of:
one or more processes running on the given computing device;
software installed on the given computing device; and
hardware installed for the given computing device.
6. The apparatus of claim 1 wherein determining the state of the given computing device comprises collecting information regarding at least one of:
central processing unit utilization by the given computing device;
memory utilization by the given computing device;
storage utilization by the given computing device;
network utilization by the given computing device.
7. The apparatus of claim 1 wherein the machine learning-based predictive model comprises a multi-variate logistic regression classifier.
8. The apparatus of claim 7 wherein the multi-variate logistic regression classifier is configured to assign a classification to an incident comprising application of a software update to an associated computing device based at least in part on a set of two or more features representing the state of the associated computing device.
9. The apparatus of claim 1 wherein the visual indicator comprises a user interface feature which, when selected, provides information regarding the assessed compatibility of the at least one software update with the given computing device.
10. The apparatus of claim 1 wherein the visual indicator comprises a warning indicator responsive to the assessed compatibility of the at least one software update with the given computing device having at least the designated threshold probability of negatively affecting operation of the given computing device.
11. The apparatus of claim 1 wherein the one or more characteristics of the at least one software update comprise one or more of:
a severity of the at least one software update;
a criticality of the at least one software update; and
an importance of software that is updated by the least one software update.
12. The apparatus of claim 1 wherein the recommendation notification is provided to the given computing device.
13. The apparatus of claim 1 wherein the recommendation notification is provided to an additional computing device of a user responsible for managing software of the given computing device.
14. A computer program product comprising a non-transitory processor-readable storage medium having stored therein program code of one or more software programs, wherein the program code when executed by at least one processing device causes the at least one processing device to perform steps of: identifying at least one software update available for a given computing device; determining a state of the given computing device; utilizing a machine learning-based predictive model to assess compatibility of the at least one software update with the given computing device based at least in part on the state of the given computing device, the machine learning-based predictive model being trained utilizing historical incident data for a plurality of incidents associated with application of software updates to a plurality of computing devices; generating a recommendation notification indicating compatibility of the at least one software update with the given computing device, wherein generating the recommendation notification comprises selecting a type of visual indicator based at least in part on the assessed compatibility indicating that the at least one software update has at least a designated threshold probability of affecting operation of the given computing device; and providing the recommendation notification in conjunction with presentation of one or more user interface features controlling whether to apply the at least one software update to the given computing device, wherein providing the recommendation notification in conjunction with presentation of the one or more user interface features controlling whether to apply the at least one software update to the given computing device comprises displaying the visual indicator; wherein the designated threshold probability is dynamically adjusted based at least in part on one or more characteristics of the at least one software update.
15. The computer program product of claim 14 wherein the machine learning-based predictive model comprises a multi-variate logistic regression classifier, the multi-variate logistic regression classifier being configured to assign a classification to an incident comprising application of a software update to an associated computing device based at least in part on a set of two or more features representing the state of the associated computing device.
16. A method comprising steps of: identifying at least one software update available for a given computing device; determining a state of the given computing device; utilizing a machine learning-based predictive model to assess compatibility of the at least one software update with the given computing device based at least in part on the state of the given computing device, the machine learning-based predictive model being trained utilizing historical incident data for a plurality of incidents associated with application of software updates to a plurality of computing devices; generating a recommendation notification indicating compatibility of the at least one software update with the given computing device, wherein generating the recommendation notification comprises selecting a type of visual indicator based at least in part on the assessed compatibility indicating that the at least one software update has at least a designated threshold probability of affecting operation of the given computing device; and providing the recommendation notification in conjunction with presentation of one or more user interface features controlling whether to apply the at least one software update to the given computing device, wherein providing the recommendation notification in conjunction with presentation of the one or more user interface features controlling whether to apply the at least one software update to the given computing device comprises displaying the visual indicator; wherein the designated threshold probability is dynamically adjusted based at least in part on one or more characteristics of the at least one software update; and wherein the method is performed by at least one processing device comprising a processor coupled to a memory.
17. The method of claim 16 wherein the machine learning-based predictive model comprises a multi-variate logistic regression classifier, the multi-variate logistic regression classifier being configured to assign a classification to an incident comprising application of a software update to an associated computing device based at least in part on a set of two or more features representing the state of the associated computing device.
18. The computer program product of claim 14 wherein the one or more characteristics of the at least one software update comprise one or more of:
a severity of the at least one software update;
a criticality of the at least one software update; and
an importance of software that is updated by the least one software update.
19. The method of claim 16 wherein the visual indicator comprises a warning indicator responsive to the assessed compatibility of the at least one software update with the given computing device having at least the designated threshold probability of negatively affecting operation of the given computing device.
20. The method of claim 16 wherein the one or more characteristics of the at least one software update comprise one or more of:
a severity of the at least one software update;
a criticality of the at least one software update; and
an importance of software that is updated by the least one software update.