Training a Model for Use with a Software Installation Process

Patent metadata

Field	Value
Publication number	US-2022129337-A1
Application number	US-201917431272-A
Country	US
Kind code	A1
Filing date	Feb 21, 2019
Priority date	Feb 21, 2019
Publication date	Apr 28, 2022
Grant date	—

Abstract

There is provided a method of training a model for use with a software installation process. A software installation process is run a plurality of times (102). Each time the software installation process is run, one parameter in a set of parameters with which the software installation process is rum is changed to generate a respective software installation process output (104). Each software installation process output is used with its respective set of parameters to train a model (106). The model is trained to identify one or more parameters that are a cause of a failed software installation process based on the output of the failed software installation process.

First claim

1 - 25 . (canceled)

26 . A method comprising:
running a software installation process a plurality of times, the software installation process dependent on a set of parameters and wherein each running results in a respective software installation process output and is based on changing a single parameter in the set of parameters, such that each running has a corresponding parameter set; and training a machine-learning model by executing a model training algorithm that uses the respective software installation process outputs and the corresponding parameter sets, by which the machine-learning model learns correlations between respective ones of parameters and failures of the software installation process.

27 . The method of claim 26 , further comprising using the machine-learning model to identify which one or more parameters in the set of parameters are most likely the cause of failure in a subsequent failed running of the software installation process.

28 . The method of claim 26 , the method further comprising generating a label vector to represent the set of parameters.

29 . The method of claim 28 , wherein the label vector comprises a plurality of items, each item representative of a parameter in the set of parameters.

30 . The method of claim 29 , wherein each time one parameter in the set of parameters is changed, the item representative of the changed parameter in the set of parameters has a first value and the items representative of all other parameters in the set of parameters have a second value, wherein the second value is different than the first value.

31 . The method of claim 26 , the method further comprising converting each software installation process output into a feature vector.

32 . The method of claim 31 , wherein the feature vector comprises a plurality of items, each item representative of a feature of the software installation process output and having a value indicative of whether the item represents a particular feature of the software installation process output.

33 . The method of claim 32 , wherein each item representative of the particular feature of the software installation process output has a first value and each item representative of other features of the software installation process output has a second value, wherein the second value is different than the first value.

34 . The method of claim 26 , wherein the machine-learning model is further trained to indicate probabilities that a particular one or ones among the respective parameters are associated with a given failure of the software installation process.

35 . The method of claim 26 , the method further comprising further training the machine-learning model based on feedback from a user, wherein the feedback from the user comprises an indication of a failure of the software installation process and the corresponding parameter set.

36 . The method of claim 26 , wherein running the software installation process a plurality of times results in a mix of installation failures and installation successes, and wherein the method further comprises filtering the respective software installation process outputs for the installation failures, based on the respective software installation outputs for the installation successes.

37 . The method of claim 26 , wherein running the software installation process comprises running a new installation process or an upgrade process.

38 . A system for training a machine-learning model for use with a software installation process, the system comprising:
processing circuitry; and at least one memory for storing instructions which, when executed by the processing circuitry, cause the system to:
run the software installation process a plurality of times, the software installation process dependent on a set of parameters and wherein each running results in a respective software installation process output and is based on changing a single parameter in the set of parameters, such that each running has a corresponding parameter set; and
training the machine-learning model by executing a model training algorithm that uses the respective software installation process outputs and the corresponding parameter sets, by which the machine-learning model learns correlations between respective ones of parameters and failures of the software installation process.

39 . A method of using a trained machine-learning model with a software installation process, the trained model trained to identify correlations between failures of the software installation process and respective parameters with which the software installation process is run, the method comprising:
running the software installation process with a set of parameters, resulting in a respective software installation process output that depends on the set of parameters; and in response to a failure of the software installation process:
using the trained machine-learning model with the respective software installation process output, to identify which one or more parameters in the set of parameters caused the failure.

40 . The method of claim 39 , wherein the trained machine-learning model generates a label vector comprising a plurality of items, each item representative of a parameter in the set of parameters and having a value indicative of whether the parameter caused the software installation process to fail.

41 . The method of claim 39 , wherein the trained machine-learning model indicates a probability that a particular one or one of the respective parameters caused the failure.

42 . The method of claim 41 , wherein the trained machine-learning model generates a label vector comprising a plurality of items, each item representative of a respective parameter in the set of parameters and having a value indicative of a probability that the respective parameter caused the software installation process to fail.

43 . A system for using a trained machine-learning model with a software installation process, the trained model trained to identify correlations between failures of the software installation process and respective parameters with which the software installation process is run, and the system comprising:
processing circuitry; and at least one memory for storing instructions which, when executed by the processing circuitry, cause the system to:
run the software installation process with a set of parameters, resulting in a respective software installation output that depends on the set of parameters; and
in response to a failure of the software installation process:
use the trained model with the respective software installation output, to identify which one or more parameters in the set of parameters are a cause of the failure.

Assignees

• Ericsson Telefon Ab L M

Inventors

• Hermanns Nikolas
• Mångs Jan-Erik
• Behrens Stefan

Classifications

• G06F18/214

  Generating training patterns; Bootstrap methods, e.g. bagging or boosting · CPC title

• G06N3/09

  Supervised learning · CPC title

• G06N3/0499

  Feedforward networks · CPC title

• G06F11/0751

  Error or fault detection not based on redundancy (power supply failures G06F1/30; network fault management H04L41/06) · CPC title

• G06N3/08

  Learning methods · CPC title