SYSTEM AND METHOD FOR DEBT MANAGEMENT

20210295426

17/130756

December 22, 2020

The present invention relates to a system and method for application debt management with zero maintenance strategy that make the applications “fit for use” and “fit for purpose”. The objective is to ensure that applications run at the lowest cost, deliver maximum performance and serve the purpose for which it was developed. The machine learning enabled debt engine of present system reads the unstructured ticket data or debts, eliminates noise, and classify the debts into one of predefined categories. This is followed by remediation of debt via either of automation or healing workbench based on predetermined priorities.

1) A system for application debt management, comprising: a memory storing program instructions; a processor configured to execute program instructions stored in the memory; a ticketing module, executed by the processor, configured to capture and analyze attributes of tickets and operational data; a debt engine, executed by the processor, configured to: identify issues from the tickets and operational data based on the attributes, automatically classify the identified issues into one or more categories based on a first set of parameters and classification rules; and prioritize the classified issues based on a second set of parameters for remediation.

2) The system as claimed in accordance with claim 1, wherein the tickets and operational data comprises ticketed and non-ticketed unstructured data.

3) The system as claimed in accordance with claim 1, wherein the debt engine is configured to contextually eliminate noises from the tickets and operational data.

4) The system as claimed in accordance with claim 1, wherein the debt engine is configured to integrate with a learning web of debt classification rules to categorize the identified issues based on technology, domain, nature of issue, root cause of problem and resolution methodology.

5) The system as claimed in accordance with claim 1, wherein the attributes of tickets and operational data comprises of technology name, ticket description, cause code, resolution method, base work pattern for pattern determination, sub work patterns or a combination thereof.

6) The system as claimed in accordance with claim 1, wherein the first set of parameters comprises of usage patterns and problem type associated with the tickets and operational data or a combination thereof.

7) The system as claimed in accordance with claim 1, wherein the debt engine utilizes a machine learning approach to classify the identified issues into one or more categories, wherein the machine learning approach may be selected from latent semantic analysis, graphical clustering, DB scan clustering, association Rule mining, stratified sampling, SVM classifier, IDF based information extractor, Jaccard similarity, rule based classification or a combination thereof.

8) The system as claimed in accordance with claim 1, wherein the debt engine is configured to apply prior experience on received tickets and operational data, read ticket pattern on set frequency and re-define the classification rules.

9) The system as claimed in accordance with claim 1, wherein the second set of parameters comprise of current cost of debt, cost of permanent fixing of debt or a combination thereof.

10) The system as claimed in accordance with claim 1, further comprising of debt remediation engine configured to create healing automation tickets based on a plurality of parameters comprising application name, technology, known error ID, country of origin, infrastructure IDs, job name, business process involved, work log or a combination thereof.

11) The system as claimed in accordance with claim 10, wherein the debt remediation engine is configured to cluster debt types of similar nature and recommends prioritization of remediation based on a plurality of parameters comprising current cost of debt, application criticality, volume of current use base or cost of permanent fixing of debt or a combination thereof.

12) A method for application debt management, comprising: capturing and analyzing attributes of tickets and operational data; identifying issues from the tickets and operational data based on the attributes; automatically classifying the identified issues into one or more categories based on a first set of parameters and classification rules; and prioritizing the classified issues based on a second set of parameters for remediation.

13) The method, as claimed in accordance with claim 12, wherein the tickets and operational data comprises ticketed and non-ticketed unstructured ticket.

14) The method, as claimed in accordance with claim 12, further comprising contextual elimination of noises from the tickets and operational data.

15) The method, as claimed in accordance with claim 12, wherein the identified issues are categorized based on technology, domain, nature of issue, root cause of problem and resolution methodology.

16) The method, as claimed in accordance with claim 12, wherein the attributes of tickets and operational data comprises of technology name, ticket description, cause code, resolution method, base work pattern for pattern determination, sub work patterns or a combination thereof.

17) The method, as claimed in accordance with claim 12, wherein the first set of parameters comprises of usage patterns and problem type associated with the tickets and operational data or a combination thereof.

18) The method, as claimed in accordance with claim 12, wherein the identified issues are classified based on machine learning approaches be selected from latent semantic analysis, graphical clustering, DB scan clustering, association Rule mining, stratified sampling, SVM classifier, IDF based information extractor, Jaccard similarity, rule based classification or a combination thereof.

19) The method, as claimed in accordance with claim 12, comprising applying prior experience on received tickets and operational data, reading ticket pattern on set frequency and re-defining the classification rules.

20) The method, as claimed in accordance with claim 12, wherein the second set of parameters comprises of current cost of debt, cost of permanent fixing of debt or a combination thereof.

21) The method, as claimed in accordance with claim 12, further comprising creating healing/automation tickets based on a plurality of parameters comprising application name, technology, known error ID, country of origin, infrastructure IDs, job name, business process involved, work log or a combination thereof.

22) The method, as claimed in accordance with claim 12, further comprising clustering debt types of similar nature and recommending prioritization of remediation based on a plurality of parameters comprising current cost of debt, application criticality, volume of current use base or cost of permanent fixing of debt or a combination thereof.

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edspap.20210295426