System and method for performing fault diagnosis on a device

A method performed in a system storing a model for estimating an appropriate handling for an error event detected in a network device, the appropriate handling estimated based on operation information collected from the network device and a history of handlings for the network device, the method comprising:

• obtaining, based on an error event detected in the network device, a diagnosis result of diagnosis in the network device for identifying handling to be performed on the error event;
• in a case where the diagnosis result indicates a plurality of candidates for handling to be performed on the error event, comparing each of the plurality of candidates for handling, to a result estimated with respect to the error event using the model;
• in a case where each of the plurality of candidates for handling the error event is found by the comparing to be different from the estimated result, obtaining partial operation information from the history;
• and
• executing machine relearning based on the obtained partial operation information with respect to a layer in a latter stage in a plurality of layers included in the model, each performing a different estimation.

A non-transitory computer readable storage medium on which is stored a computer program for causing a computer to execute a method for a system that stores a model for estimating an appropriate handling for an error event detected in a network device, the model constructed by machine learning using operation information collected from the network device and a history of handlings for the network device, the method comprising:

• obtaining, based on an error event detected in the network device, a diagnosis result of diagnosis in the network device for identifying handling to be performed on the error event;
• in a case where the diagnosis result indicates a plurality of candidates for handling to be performed on the error event, comparing each of the plurality of candidates for handling, to a result estimated with respect to the error event using the model;
• in a case where each of the plurality of candidates for handling the error event is found by the comparing to be different from the estimated result, obtaining partial operation information from the history;
• and
• executing machine relearning based on the obtained partial operation information with respect to a layer in a latter stage in a plurality of layers included in the model, each performing a different estimation.