Firmware failure reason prediction using machine learning techniques
Techniques are provided for predicting firmware installation failure reasons using machine learning techniques. One method comprises obtaining log data for a user device, wherein the log data is obtained following a failure of a firmware installation on the user device; extracting a plurality of features from the obtained log data; applying the extracted features to a trained machine learning model to obtain a prediction of whether the firmware installation failure is caused by a hardware-related failure or a software-related failure; and performing an automated remedial action based on a result of the prediction. The trained machine learning model can be trained using historical data for multiple user devices that experienced a firmware installation failure, where the historical data comprises a label indicating whether a given failure comprises a hardware-related failure or a software-related failure. The trained machine learning model can be trained and tested using cross-validation techniques.
1. A method, comprising:
obtaining log data for a user device, wherein the log data is obtained following a failure of a firmware installation on the user device;
extracting a plurality of features from the obtained log data;
applying the extracted features to one or more trained machine learning models to obtain a prediction of whether the firmware installation failure is caused by a hardware-related failure or a software-related failure; and
performing one or more automated remedial actions based at least in part on a result of the prediction;
wherein the method is performed by at least one processing device comprising a processor coupled to a memory.
2. The method of claim 1 , wherein the obtained log data is obtained from one or more of the user device and a system that tested the firmware of the user device.
3. The method of claim 1 , wherein the extracting the plurality of features further comprises one or more of embedding and normalizing at least some of the plurality of features.
4. The method of claim 1 , wherein the firmware installation failure on the user device occurs one or more of following an initial installation of the firmware and following an update of the firmware.
5. The method of claim 1 , wherein the one or more automated remedial actions comprise initiating one or more of: reinstalling the firmware, performing diagnostics on the user device, repairing the user device and replacing one or more components of the user device.
6. The method of claim 1 , wherein the one or more trained machine learning models are trained using historical data for a plurality of user devices that experienced a firmware installation failure, wherein the historical data comprises a label indicating whether a given failure comprises a hardware-related failure or a software-related failure.
7. The method of claim 1 , wherein the one or more trained machine learning models are trained and tested using cross-validation techniques.
8. An apparatus comprising:
at least one processing device comprising a processor coupled to a memory;
the at least one processing device being configured to implement the following steps:
obtaining log data for a user device, wherein the log data is obtained following a failure of a firmware installation on the user device;
extracting a plurality of features from the obtained log data;
applying the extracted features to one or more trained machine learning models to obtain a prediction of whether the firmware installation failure is caused by a hardware-related failure or a software-related failure; and
performing one or more automated remedial actions based at least in part on a result of the prediction.
9. The apparatus of claim 8 , wherein the obtained log data is obtained from one or more of the user device and a system that tested the firmware of the user device.
10. The apparatus of claim 8 , wherein the extracting the plurality of features further comprises one or more of embedding and normalizing at least some of the plurality of features.
11. The apparatus of claim 8 , wherein the firmware installation failure on the user device occurs one or more of following an initial installation of the firmware and following an update of the firmware.
12. The apparatus of claim 8 , wherein the one or more automated remedial actions comprise initiating one or more of: reinstalling the firmware, performing diagnostics on the user device, repairing the user device and replacing one or more components of the user device.
13. The apparatus of claim 8 , wherein the one or more trained machine learning models are trained using historical data for a plurality of user devices that experienced a firmware installation failure, wherein the historical data comprises a label indicating whether a given failure comprises a hardware-related failure or a software-related failure.
14. The apparatus of claim 8 , wherein the one or more trained machine learning models are trained and tested using cross-validation techniques.
15. 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 the following steps:
obtaining log data for a user device, wherein the log data is obtained following a failure of a firmware installation on the user device;
extracting a plurality of features from the obtained log data;
applying the extracted features to one or more trained machine learning models to obtain a prediction of whether the firmware installation failure is caused by a hardware-related failure or a software-related failure; and
performing one or more automated remedial actions based at least in part on a result of the prediction.
16. The non-transitory processor-readable storage medium of claim 15 , wherein the obtained log data is obtained from one or more of the user device and a system that tested the firmware of the user device.
17. The non-transitory processor-readable storage medium of claim 15 , wherein the extracting the plurality of features further comprises one or more of embedding and normalizing at least some of the plurality of features.
18. The non-transitory processor-readable storage medium of claim 15 , wherein the one or more automated remedial actions comprise initiating one or more of: reinstalling the firmware, performing diagnostics on the user device, repairing the user device and replacing one or more components of the user device.
19. The non-transitory processor-readable storage medium of claim 15 , wherein the one or more trained machine learning models are trained using historical data for a plurality of user devices that experienced a firmware installation failure, wherein the historical data comprises a label indicating whether a given failure comprises a hardware-related failure or a software-related failure.
20. The non-transitory processor-readable storage medium of claim 15 , wherein the one or more trained machine learning models are trained and tested using cross-validation techniques.