Generating a score for a runbook or a script
The present disclosure relates to system(s) and method(s) for generating a score for a runbook or a script. The system receives a ticket and a ticket description. The system further identifies a set of policies based on an analysis of historical data, the ticket and the ticket description. Further, the system determines a set of functions based on an analysis of the set of policies using a neural network technique and an Inverse Reinforcement Learning technique. Furthermore, the system recommends a runbook or a script based on the set of functions, the ticket and the ticket description. The runbook or the script is further executed to resolve the ticket. Based on the execution, the system records a script success or a script failure. The system further generates a score for the runbook or the script based on the script success or the script failure.
1. A system for generating a score for one of a runbook or a script, the system comprising:
a memory;
a processor coupled to the memory, wherein the processor is configured to execute programmed instructions stored in the memory to:
receive ticket data, wherein the ticket data comprises a ticket, and a ticket description;
identify a set of policies associated with a platform, wherein the set of policies is identified based on an analysis of historical data, the ticket and the ticket description;
determine a set of functions based on an analysis of the set of policies, wherein the set of policies are analysed using a neural network technique and an Inverse Reinforcement Learning technique;
recommend one of a runbook or a script from a runbook recommendation engine based on an analysis of the ticket and the ticket description, wherein the ticket and the ticket description are analysed using the set of functions;
identify a set of hyperparameter values associated with the platform, wherein the set of hyperparameter values is further used to implement the runbook recommendation engine over a new platform, and wherein a transfer learning technique is used for the implementation of the runbook recommendation engine;
execute the runbook or the script to resolve the ticket;
record one of a script success or a script failure based on the execution of the runbook or the script, wherein the script success is recorded when the runbook or the script resolves the ticket, and wherein the failure script is recorded when the runbook or the script fails to resolve the ticket; and
generate a score associated with one of the runbook or the script, wherein the score is generated based on one of the script success or the script failure.
2. The system as claimed in claim 1 , further configured to identify a set of algorithms based on an analysis of the set of functions and the historical data using the Inverse Reinforcement Learning technique, wherein the set of algorithms is used to recommend the runbook or the script.
3. The system as claimed in claim 1 , wherein the historical data, the ticket and the ticket description are analysed using a neural network technique and the Inverse Reinforcement Learning technique.
4. The system as claimed in claim 1 , further configured to penalize or boost the score in the runbook recommendation engine based on the script success and script failure.
5. The system as claimed in claim 1 , further configured to update the score based on feedback received from a user upon execution of the runbook or the script.
6. A method to generate a score for one of a runbook or a script, the method comprises:
receiving, by a processor, ticket data, wherein the ticket data comprises a ticket, and a ticket description;
identifying, by the processor, a set of policies associated with a platform, wherein the set of policies is identified based on an analysis of historical data, the ticket and the ticket description;
determining, by the processor, a set of functions based on an analysis of the set of policies, wherein the set of policies are analysed using a neural network technique and an Inverse Reinforcement Learning technique;
recommending, by the processor, one of a runbook or a script from a runbook recommendation engine based on an analysis of the ticket and the ticket description, wherein the ticket and the ticket description are analysed using the set of functions;
identifying, by the processor, a set of hyperparameter values associated with the platform, wherein the set of hyperparameter values is further used to implement the runbook recommendation engine over a new platform, and wherein a transfer learning technique is used for the implementation of the runbook recommendation engine;
executing, by the processor, the runbook or the script to resolve the ticket;
recording, by the processor, one of a script success or a script failure based on the execution of the runbook or the script, wherein the script success is recorded when the runbook or the script resolves the ticket, and wherein the failure script is recorded when the runbook or the script fails to resolve the ticket; and
generating, by the processor, a score associated with one of the runbook or the script, wherein the score is generated based on one of the script success or the script failure.
7. The method as claimed in claim 6 , further comprises identifying a set of algorithms based on an analysis of the set of functions and the historical data using the Inverse Reinforcement Learning technique, wherein the set of algorithms is used to recommend the runbook or the script.
8. The method as claimed in claim 6 , wherein the historical data, the ticket and the ticket description are analysed using a neural network technique and an Inverse Reinforcement Learning technique.
9. The method as claimed in claim 6 , further comprises penalizing or boosting the score in the runbook recommendation engine based on the script success and script failure.
10. The method as claimed in claim 6 , further comprises updating the score based on feedback received from a user upon execution of the runbook or the script.