Applying consistent log levels to application log messages

• US 10,318,405 B2
• Filed: 08/24/2016
• Issued: 06/11/2019
• Est. Priority Date: 08/24/2016
• Status: Active Grant

• Current Assignee: International Business Machines Corporation
• Original Assignee: International Business Machines Corporation
• Inventors: Allen, Corville O., Freed, Andrew R., Gerard, Scott N., Miller, Dorian B.
• Primary Examiner(s): Bonzo, Bryce P
• Assistant Examiner(s): Arcos, Jeison C
• Application Number:
  US15/245,673
• Publication Number:
  US 20180060211A1
• Time in Patent Office: 1,021 Days
• CPC Class Codes: G06F 11/07 Responding to the occurrence of a fault, e.g. fault tolerance
• CPC Class Codes: G06F 11/0781 Error filtering or prioritizing based on a policy defined by the user or on a policy defined by a hardware/software module, e.g. according to a severity level
• CPC Class Codes: G06F 11/3636 by tracing the execution of the program
• CPC Class Codes: G06F 11/3664 Environments for testing or debugging software
• CPC Class Codes: G06F 40/211 Syntactic parsing, e.g. based on context-free grammar [CFG] or unification grammars
• CPC Class Codes: G06F 40/247 Thesauruses; Synonyms

Abstract

A method is provided for analyzing log message content. The computer-implemented method may include detecting, by at least one processor, log messages in an application code, identifying a log level assigned to each of the log messages, and performing natural language processing (NLP) analysis on each of the log messages by using at least keyword and synonym matching percentage analysis criteria. The computer-implemented method may further include determining, in response to the NLP analysis, a severity score of each of the log messages, and reclassifying, based on the severity score, the assigned log level of one or more of the log messages to a different log level.

Claims

1. A computer-implemented method executed on a machine having at least one processor, a memory, and a communication platform for analyzing log message content, the method comprising:

detecting, by the at least one processor, log messages in an application code;

identifying, by the at least one processor, a log level assigned to each of the log messages by a user, via a log level assignment component;

performing natural language processing (NLP) analysis on each of the log messages, via an NLP analysis tool, by employing training data in combination with at least keyword and synonym matching percentage analysis criteria;

determining, in response to the NLP analysis, a severity score of each of the log messages via a score classification component;

comparing content of the log messages with content of log messages derived from other application code written by other users;

automatically reclassifying, via a reclassification device, and based on the severity score and the compared content, the assigned log level of one or more of the log messages to a different log level; and

outputting reclassified log messages to an interface of a computing device.

2. The method of claim 1, further comprising using an integrated development environment (IDE) to reclassify the assigned log level of one or more of the log messages.

3. The method of claim 1, further comprising applying a learning model to each of the log messages detected in the application code.

4. The method of claim 1, further comprising factoring a length of each of the log messages into the severity score.

5. The method of claim 1, further comprising factoring a location of each of the log messages within the application code into the severity score.

6. The method of claim 1, further comprising analyzing each of the log messages for content and context.

7. The method of claim 1, wherein the compared content reveal log similarities and differences that are fed back into the NLP analysis tool to bolster the training data.

8. The method of claim 7, further comprising grouping the log messages having similarities and assigning a common log level to the group of log messages having similarities.

9. The method of claim 1, wherein the keyword and synonym matching percentage analysis criteria include trigger words, syntax parameters, and sentiment parameters.

10. The method of claim 1, wherein the keyword and synonym matching percentage analysis criteria include positive and negative sentiment determination of each of the log messages of the application code.

11. A non-transitory computer readable storage medium comprising a computer readable program for analyzing log message content, wherein the computer readable program when executed on a computer causes the computer to perform the steps of:

detecting, by the at least one processor, log messages in an application code;

identifying, by the at least one processor, a log level assigned to each of the log messages by a user, via a log level assignment component;

performing natural language processing (NLP) analysis on each of the log messages, via an NLP analysis tool, by employing training data in combination with at least keyword and synonym matching percentage analysis criteria;

determining, in response to the NLP analysis, a severity score of each of the log messages via a score classification component;

comparing content of the log messages with content of log messages derived from other application code written by other users;

automatically reclassifying, via a reclassification device, and based on the severity score and the compared content, the assigned log level of one or more of the log messages to a different log level; and

outputting reclassified log messages to an interface of a computing device.

12. The non-transitory computer readable storage medium of claim 11, further comprising using an integrated development environment (IDE) to reclassify the assigned log level of one or more of the log messages.

13. The non-transitory computer readable storage medium of claim 11, further comprising applying a learning model to each of the log messages detected in the application code.

14. The non-transitory computer readable storage medium of claim 11, further comprising factoring a length of each of the log messages into the severity score.

15. The non-transitory computer readable storage medium of claim 11, further comprising factoring a location of each of the log messages within the application code into the severity score.

16. The non-transitory computer readable storage medium of claim 11, further comprising analyzing each of the log messages for content and context.

17. The non-transitory computer readable storage medium of claim 11, wherein the compared content reveal log similarities and differences that are fed back into the NLP analysis tool to bolster the training data.

18. The non-transitory computer readable storage medium of claim 17, further comprising grouping the log messages having similarities and assigning a common log level to the group of log messages having similarities.

19. The non-transitory computer readable storage medium of claim 11, wherein the keyword and synonym matching percentage analysis criteria include trigger words, syntax parameters, and sentiment parameters.

20. A computer system for analyzing log message content, the computer system comprising:

a memory; and

a processor in communication with the memory, wherein the processor is configured to;

detect, by the at least one processor, log messages in an application code;

identify, by the at least one processor, a log level assigned to each of the log messages by a user, via a log level assignment component;

perform natural language processing (NLP) analysis on each of the log messages via an NLP analysis tool, by employing training data in combination with at least keyword and synonym matching percentage analysis criteria;

determine, in response to the NLP analysis, a severity score of each of the log messages via a score classification component;

compare content of the log messages with content of log messages derived from other application code written by other users;

automatically reclassify, via a reclassification device, and based on the severity score and the compared content, the assigned log level of one or more of the log messages to a different log level; and

output reclassified log messages to an interface of a computing device.