Learning based incident or defect resolution, and test generation

1. A learning based incident or defect resolution, and test generation apparatus comprising:

a log data receiver, executed by at least one hardware processor, to ascertain historical log data that includes incident or defect log data associated with operation of a process;

a step action graph generator, executed by the at least one hardware processor, to generate, based on the historical log data, step action graphs;

an incident and defect action graph generator, executed by the at least one hardware processor, to generate, based on grouping of the step action graphs with respect to different incident and defect tickets, an incident and defect action graph for each of the incident and defect tickets; and

an output generator, executed by the at least one hardware processor, to generate, based on an analysis of a new incident or defect with respect to each of the incident and defect action graphs corresponding to the incident and defect tickets, an output to resolve the new incident or defect.

2. The learning based incident or defect resolution, and test generation apparatus according to claim 1 , wherein the step action graph generator is executed by the at least one hardware processor to generate, based on the historical log data, the step action graphs by:

analyzing, from the historical log data, data related to at least one of resolution of an incident or defect, or data related to reproduction of the incident or defect; and

transforming, from the analyzed data, textual details from each step into the step action graphs by identifying

action terms,

subjects initiating the action terms, and

objects associated with the actions terms.

3. The learning based incident or defect resolution, and test generation apparatus according to claim 1 , wherein the step action graph generator is executed by the at least one hardware processor to generate, based on the historical log data, the step action graphs by:

identifying adjacent action terms; and

joining nodes related to the adjacent action terms by sequential flow edges.

4. The learning based incident or defect resolution, and test generation apparatus according to claim 1 , wherein the step action graph generator is executed by the at least one hardware processor to generate, based on the historical log data, the step action graphs by:

identifying conditionally related action terms; and

joining nodes related to the conditionally related action terms by conditional branching.

5. The learning based incident or defect resolution, and test generation apparatus according to claim 1 , wherein the incident and defect action graph generator is executed by the at least one hardware processor to generate, based on grouping of the step action graphs with respect to different incident and defect tickets, the incident and defect action graph for each of the incident and defect tickets by:

analyzing, for the step action graphs, directed flow edges that identify sequences of actions; and

generating, based on grouping of the step action graphs according to the directed flow edges that identify the sequences of actions and with respect to the different incident and defect tickets, the incident and defect action graph for each of the incident and defect tickets.

6. The learning based incident or defect resolution, and test generation apparatus according to claim 1 , further comprising:

a machine learning model generator, executed by the at least one hardware processor, to generate a machine learning model by:

learning non-linear patterns of actions to resolve an incident or defect from the historical log data.

7. The learning based incident or defect resolution, and test generation apparatus according to claim 1 , wherein the output generator is executed by the at least one hardware processor to generate, based on the analysis of the new incident or defect with respect to each of the incident and defect action graphs corresponding to the incident and defect tickets, the output to resolve the new incident or defect by:

determining semantic relatedness between the new incident or defect, and incidents or defects, respectively, of the historical log data; and

determining whether selection of a step from the historical log data causally constrains selection of a subsequent step from the historical log data.

8. The learning based incident or defect resolution, and test generation apparatus according to claim 7 , wherein the output generator is executed by the at least one hardware processor to determine whether selection of the step from the historical log data causally constrains selection of the subsequent step from the historical log data by:

determining a number of sequences where the step from the historical log data precedes the subsequent step from the historical log data; and

assigning, based on the number of sequences, a degree of confidence to the step from the historical log data that precedes the subsequent step from the historical log data.

9. The learning based incident or defect resolution, and test generation apparatus according to claim 1 , wherein the output generator is executed by the at least one hardware processor to generate, based on the analysis of the new incident or defect with respect to each of the incident and defect action graphs corresponding to the incident and defect tickets, the output to resolve the new incident or defect by:

analyzing step to defect similarity between a step of the historical log data and the new incident or defect; and

analyzing defect to defect similarity between a defect of the historical log data and the new incident or defect.

10. The learning based incident or defect resolution, and test generation apparatus according to claim 1 , further comprising:

a user feedback analyzer, executed by the at least one hardware processor, to

identify, in the generated output, an incomplete feature of the generated output;

ascertain feedback with respect to the incomplete feature;

update, based on the feedback, the historical log data with respect to the incomplete feature; and

update a machine learning model based on an analysis of the updated historical log data.

11. The learning based incident or defect resolution, and test generation apparatus according to claim 10 , wherein the user feedback analyzer is executed by the at least one hardware processor to:

generate a further incident and defect action graph with respect to the new incident or defect;

identify, with respect to the incomplete feature, a difference in an existing incident and defect action graph and the further incident and defect action graph; and

continue iterations related to the update of the historical log data with respect to a further incomplete feature and the update of the machine learning model with respect to a further update to the historical log data until elimination of differences in existing incident and defect action graphs and further incident and defect action graphs related to the new incident or defect.

12. A computer implemented method for learning based incident or defect resolution, and test generation comprising:

ascertaining, by at least one hardware processor, historical log data that includes existing tests;

generating, by the at least one hardware processor, based on the historical log data, step action graphs;

generating, by the at least one hardware processor, based on grouping of the step action graphs with respect to different tests, a test action graph for each of the different tests; and

identifying, by the at least one hardware processor, based on an analysis of the historical log data with respect to each of the test action graphs corresponding to the different tests, a defect related to software.

13. The computer implemented method according to claim 12 , further comprising:

generating, by the at least one hardware processor, based on an analysis of a machine learning model with respect to a new test related to the software, an output, further comprises:

ascertaining a test objective and test data for the new test; and

generating, based on the analysis of the machine learning model with respect to the test objective and the test data for the new test related to the software, the output.

14. A non-transitory computer readable medium having stored thereon machine readable instructions, the machine readable instructions, when executed by at least one hardware processor, cause the at least one hardware processor to:

ascertain historical log data that includes incident or defect log data associated with operation of a process;

generate, based on the historical log data, step action graphs;

generate, based on grouping of the step action graphs with respect to different incident and defect tickets, an incident and defect action graph for each of the incident and defect tickets; and

generate, based on an analysis of a new incident or defect with respect to each of the incident and defect action graphs corresponding to the incident and defect tickets, an output that includes a sequence of actions to identify a root cause of the new incident or defect.

15. The non-transitory computer readable medium according to claim 14 , wherein the machine readable instructions to generate, based on the historical log data, the step action graphs, when executed by the at least one hardware processor, further cause the at least one hardware processor to:

analyze, from the historical log data, data related to at least one of resolution of an incident or defect, or data related to reproduction of the incident or defect; and

transform, from the analyzed data, textual details from each step into the step action graphs by identifying

action terms,

subjects initiating the action terms, and

objects associated with the actions terms.

16. The non-transitory computer readable medium according to claim 14 , wherein the machine readable instructions to generate, based on the historical log data, the step action graphs, when executed by the at least one hardware processor, further cause the at least one hardware processor to:

identify adjacent action terms; and

join nodes related to the adjacent action terms by sequential flow edges.

17. The non-transitory computer readable medium according to claim 14 , wherein the machine readable instructions to generate, based on the historical log data, the step action graphs, when executed by the at least one hardware processor, further cause the at least one hardware processor to:

identify conditionally related action terms; and

join nodes related to the conditionally related action terms by conditional branching.

18. The non-transitory computer readable medium according to claim 14 , wherein the machine readable instructions to generate, based on grouping of the step action graphs with respect to different incident and defect tickets, the incident and defect action graph for each of the incident and defect tickets, when executed by the at least one hardware processor, further cause the at least one hardware processor to:

analyze, for the step action graphs, directed flow edges that identify sequences of actions; and

generate, based on grouping of the step action graphs according to the directed flow edges that identify the sequences of actions and with respect to the different incident and defect tickets, the incident and defect action graph for each of the incident and defect tickets.

19. The non-transitory computer readable medium according to claim 14 , wherein the machine readable instructions to generate, based on the analysis of the new incident or defect with respect to each of the incident and defect action graphs corresponding to the incident and defect tickets, the output that includes the sequence of actions to identify the root cause of the new incident or detect, when executed by the at least one hardware processor, further cause the at least one hardware processor to:

determine semantic relatedness between the new incident or defect, and incidents or defects, respectively, of the historical log data; and

determine whether selection of a step from the historical log data causally constrains selection of a subsequent step from the historical log data.

20. The non-transitory computer readable medium according to claim 19 , wherein the machine readable instructions to determine whether selection of the step from the historical log data causally constrains selection of the subsequent step from the historical log data, when executed by the at least one hardware processor, further cause the at least one hardware processor to:

determine a number of sequences where the step from the historical log data precedes the subsequent step from the historical log data; and

assign, based on the number of sequences, a degree of confidence to the step from the historical log data that precedes the subsequent step from the historical log data.