Technology for logging levels and transaction log files

1. A computer system implemented method of dynamic logging including:

generating parsed event data by at least one natural language (NL) processor responsive to event data of a log for transactions of a target application;

classifying, by a first classifier applying a first machine learning model to the parsed event data, context states of the respective transactions of the target application;

classifying, by a second classifier applying a second machine learning model to the parsed event data and the context states, trouble prone states of the respective transactions, wherein the trouble prone states are for respective hierarchical levels;

determining, by a logic module, for a current one of the trouble prone states for a current transaction, that the current trouble prone state is a higher trouble prone level than for a transaction immediately preceding the current transaction; and

sending, by the logic module to the target application, an increased log detail selection responsive to the determining that the current trouble prone state is the higher trouble prone level, so that a greater amount of log detail is logged for at least a next transaction after the current transaction.

2. The method of claim 1 , wherein the context states of respective transactions of the target application include a security context.

3. The method of claim 1 , wherein the context states of respective transactions of the target application include a technical context.

4. The method of claim 1 , wherein the context states of respective transactions of the target application include a functional context.

5. The method of claim 1 , wherein the hierarchical levels include at least a lowest level, a highest level and a level between the lowest and highest levels.

6. The method of claim 1 , comprising:

determining, by the logic module, for a current one of the trouble prone states for a current transaction, that the current trouble prone state is a lower trouble prone level than for a transaction immediately preceding the current transaction; and

sending, by the dynamic logging level logic to the target application, a decreased log detail selection responsive to the determining that the current trouble prone state is the lower trouble prone level, so that a decreased amount of log detail is logged for at least a next transaction after the current transaction.

7. The method of claim 1 , comprising:

generating the parsed event data by the at least one NL processor includes generating the parsed event data by a NL processor of the logic module.

8. The method of claim 1 , wherein the first and second machine learning models include respective clustering models.

9. A computer system for dynamic logging, the system comprising:

a processor; and

a computer readable storage medium connected to the processor, wherein the computer readable storage medium has stored thereon a program for controlling the processor, and wherein the processor is operative with the program to execute the program for:

generating parsed event data by at least one natural language (NL) processor responsive to event data of a log for transactions of a target application;

classifying, by a first classifier applying a first machine learning model to the parsed event data, context states of the respective transactions of the target application;

classifying, by a second classifier applying a second machine learning model to the parsed event data and the context states, trouble prone states of the respective transactions, wherein the trouble prone states are for respective hierarchical levels;

determining, by a logic module, for a current one of the trouble prone states for a current transaction, that the current trouble prone state is a higher trouble prone level than for a transaction immediately preceding the current transaction; and

sending, by the logic module to the target application, an increased log detail selection responsive to the determining that the current trouble prone state is the higher trouble prone level, so that a greater amount of log detail is logged for at least a next transaction after the current transaction.

10. The computer system of claim 9 , wherein the context states of respective transactions of the target application include a security context, a technical context and a functional context.

11. The computer system of claim 9 , wherein the hierarchical levels include at least a lowest level, a highest level and a level between the lowest and highest levels.

12. The computer system of claim 9 , wherein the processor is operative with the program to execute the program for:

determining, by the logic module, for a current one of the trouble prone states for a current transaction, that the current trouble prone state is a lower trouble prone level than for a transaction immediately preceding the current transaction; and

sending, by the dynamic logging level logic to the target application, a decreased log detail selection responsive to the determining that the current trouble prone state is the lower trouble prone level, so that a decreased amount of log detail is logged for at least a next transaction after the current transaction.

13. The computer system of claim 9 , wherein generating the parsed event data by the at least one NL processor includes generating the parsed event data by a NL processor of the logic module.

14. The computer system of claim 9 , wherein the first and second machine learning models include respective clustering models.

15. A computer program product for dynamic logging, the computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions executable by a processor to cause the processor to:

generate parsed event data by at least one natural language (NL) processor responsive to event data of a log for transactions of a target application;

classify, by a first classifier applying a first machine learning model to the parsed event data, context states of the respective transactions of the target application;

classify, by a second classifier applying a second machine learning model to the parsed event data and the context states, trouble prone states of the respective transactions, wherein the trouble prone states are for respective hierarchical levels;

determine, by a logic module, for a current one of the trouble prone states for a current transaction, that the current trouble prone state is a higher trouble prone level than for a transaction immediately preceding the current transaction; and

send, by the logic module to the target application, an increased log detail selection responsive to the determining that the current trouble prone state is the higher trouble prone level, so that a greater amount of log detail is logged for at least a next transaction after the current transaction.

16. The computer program product of claim 15 , wherein the context states of respective transactions of the target application include a security context, a technical context and a functional context.

17. The computer program product of claim 15 , wherein the hierarchical levels include at least a lowest level, a highest level and a level between the lowest and highest levels.

18. The computer program product of claim 15 , wherein the program instructions are executable by a processor to cause the processor to:

determine, by the logic module, for a current one of the trouble prone states for a current transaction, that the current trouble prone state is a lower trouble prone level than for a transaction immediately preceding the current transaction; and

send, by the dynamic logging level logic to the target application, a decreased log detail selection responsive to the determining that the current trouble prone state is the lower trouble prone level, so that a decreased amount of log detail is logged for at least a next transaction after the current transaction.

19. The computer program product of claim 15 , wherein generating the parsed event data by the at least one NL processor includes generating the parsed event data by a NL processor of the logic module.

20. The computer program product of claim 15 , wherein the first and second machine learning models include respective clustering models.