Method and system for automatically identifying and resolving errors in log file
A method and a system for automatically identifying and resolving at least one error in at least one log file are provided. The method includes receiving, via a communication interface, the at least one log file from at least one log store. The method further includes identifying, using a first trained model, the at least one error in the at least one log file. Next, the method includes classifying the at least one error into a first category of errors and a second category of errors. Thereafter, the method includes automatically resolving, using a second trained model, the first category of errors in the at least one log file.
1. A method for automatically identifying and resolving at least one error in at least one log file, the method being implemented by at least one processor, the method comprising:
receiving, by the at least one processor via a communication interface, the at least one log file from at least one log store;
training a first model by seeding a plurality of errors in an error store database;
training a second trained model by seeding a plurality of error fixes for a plurality of known errors in an error fix store database;
identifying, by the at least one processor using the first trained model, the at least one error in the at least one log file;
mapping, by using the first trained model, the at least one error identified in the at least one log file to errors stored in the error store database to filter out noise and then the filtered errors are further stored in an error classification store database for resolving the at least one identified error;
classifying, by the at least one processor, the at least one error into a first category of errors and a second category of errors;
mapping, by using the second trained model, the first category of errors with corresponding fix stored in the error fix store database; and
automatically resolving, by the at least one processor using the second trained model, the first category of errors in the at least one log file.
2. The method as claimed in claim 1 , wherein each of the first trained model and the second trained model corresponds to a machine learning model.
3. The method as claimed in claim 1 , wherein the at least one error is classified into the first category of errors and the second category of errors based on at least one from among complexity of errors, resolution of errors, type of errors, criticality of errors, weightage of errors, availability of possible fixes of errors, past seeded errors, frequency of occurrence of errors or any combination thereof.
4. The method as claimed in claim 1 , wherein the first category of errors comprises prime errors that are resolved automatically using the second trained model.
5. The method as claimed in claim 1 , wherein the method includes alerting, by the at least one processor, at least one entity in an event at least one random error occurs while resolving the first category of errors in the at least one log file.
6. The method as claimed in claim 1 , wherein the second category of errors comprises complex errors that require manual intervention for the resolution of errors.
7. A computing device configured to implement an execution of a method for automatically identifying and resolving at least one error in at least one log file, the computing device comprising:
a processor;
a memory; and
a communication interface coupled to each of the processor and the memory,
wherein the processor is configured to:
receive, via the communication interface, the at least one log file from at least one log store;
train a first model by seeding a plurality of errors in an error store database;
train a second trained model by seeding a plurality of error fixes for a plurality of known errors in an error fix store database;
identify, using the first trained model, the at least one error in the at least one log file;
map, by using the first trained model, the at least one error identified in the at least one log file to errors stored in the error store database to filter out noise and then the filtered errors are further stored in an error classification store database for resolving the at least one identified error;
classify the at least one error into a first category of errors and a second category of errors;
map, by using the second trained model, the first category of errors with corresponding fix stored in the error fix store database; and
automatically resolve, using the second trained model, the first category of errors in the at least one log file.
8. The computing device as claimed in claim 7 , wherein each of the first trained model and the second trained model corresponds to a machine learning model.
9. The computing device as claimed in claim 7 , wherein the processor is configured to classify the at least one error into the first category of errors and the second category of errors based on at least one from among complexity of errors, resolution of errors, type of errors, criticality of errors, weightage of errors, availability of possible fixes of errors, past seeded errors, frequency of occurrence of errors or any combination thereof.
10. The computing device as claimed in claim 7 , wherein the first category of errors comprises prime errors that are resolved automatically using the second trained model.
11. The computing device as claimed in claim 7 , wherein the processor is further configured to alert at least one entity in an event at least one random error occurs while resolving the first category of errors in the at least one log file.
12. The computing device as claimed in claim 7 , wherein the second category of errors comprises complex errors that require manual intervention for the resolution of errors.
13. A non-transitory computer readable storage medium storing instructions for automatically identifying and resolving at least one error in at least one log file, the instructions comprising executable code which, when executed by a processor, causes the processor to:
receive, via a communication interface, the at least one log file from at least one log store;
train a first model by seeding a plurality of errors in an error store database;
train a second trained model by seeding a plurality of error fixes for a plurality of known errors in an error fix store database;
identify, using the first trained model, the at least one error in the at least one log file;
map, by using the first trained model, the at least one error identified in the at least one log file to errors stored in the error store database to filter out noise and then the filtered errors are further stored in an error classification store database for resolving the at least one identified error;
classify the at least one error into a first category of errors and a second category of errors;
map, by using the second trained model, the first category of errors with corresponding fix stored in the error fix store database; and
automatically resolve, using the second trained model, the first category of errors in the at least one log file.
14. The storage medium as claimed in claim 13 , wherein each of the first trained model and the second trained model corresponds to a machine learning model.
15. The storage medium as claimed in claim 13 , wherein when executed by the processor, the exec E table code further causes the processor to classify the at least one error into the first category of errors and the second category of errors based on at least one from among complexity of errors, resolution of errors, type of errors, criticality of errors, weightage of errors, availability of possible fixes of errors, past seeded errors, frequency of occurrence of errors or any combination thereof.
16. The storage medium as claimed in claim 13 , wherein when executed by the processor, the executable code further causes the processor to alert at least one entity in an event at least one random error occurs while resolving the first category of errors in the at least one log file.
17. The storage medium as claimed in claim 13 , wherein the first category of errors comprises prime errors that are resolved automatically using the second trained model, and the second category of errors comprises complex errors that require manual intervention for the resolution of errors.