NLP-Based Log Level Reclassification for Application Logs
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing log analysis methods require manual intervention to set log levels, leading to inconsistencies across different coding styles and lack of accuracy in determining the relative importance of log messages, making it difficult to maintain consistent log levels across applications.
Innovation Solution
A computer-implemented method and system that uses natural language processing (NLP) to analyze log messages, applying keyword and synonym matching percentage analysis criteria, sentiment analysis, and severity scoring to reclassify log levels automatically, ensuring consistent log levels across applications without manual intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual intervention is used to set log levels, then flexibility in log level assignment is improved, but consistency across different coding styles deteriorates
Solution Approach 1:
The system performs self-service by automatically analyzing log messages and assigning appropriate log levels without requiring manual intervention. The NLP engine autonomously processes log messages, extracts semantic meaning, and determines suitable log levels based on the analyzed content, thereby eliminating the inconsistency caused by manual assignment while maintaining flexibility through intelligent decision-making
Solution Approach 2:
The system changes the parameter of log level assignment from manual discrete choices to automated continuous analysis. By using NLP to analyze the semantic content, sentiment, and context of log messages, the system dynamically determines appropriate log levels based on the actual meaning rather than relying on manual selection, thus achieving both consistency and adaptability
2Adaptability or versatility
If manual intervention is used to set log levels, then adaptability to specific cases is improved, but accuracy in determining relative importance deteriorates
Solution Approach 1:
The system implements feedback by using NLP analysis to continuously evaluate log message content and adjust log level assignments accordingly. The engine analyzes semantic meaning, sentiment, and contextual information to provide accurate feedback on the relative importance of each log message, thereby improving measurement precision while maintaining adaptability to specific cases through intelligent decision-making
Solution Approach 2:
The system replaces the mechanical process of manual log level assignment with an automated NLP-based analysis system. This substitution enables more accurate determination of relative importance by using natural language processing to understand the semantic meaning and context of log messages, thereby improving measurement precision while maintaining adaptability to specific cases
3Stability of the object's composition
If automated NLP analysis is used to reclassify log levels, then consistency across applications is improved, but device complexity increases
Solution Approach 1:
The system achieves universality by implementing a multi-functional NLP engine that can handle various log message formats, languages, and contexts through a single unified platform. The engine performs multiple functions including semantic analysis, sentiment analysis, entity recognition, and log level determination, thereby achieving consistency across applications without proportionally increasing complexity through modular and integrated design
4Productivity
If automated NLP analysis is used to reclassify log levels, then productivity in log analysis is improved, but use of energy increases
Solution Approach 1:
The system applies partial action by selectively analyzing only the necessary portions of log messages to determine appropriate log levels. Rather than performing exhaustive analysis on every log message, the NLP engine focuses on key semantic elements, sentiment indicators, and contextual features that are most relevant for log level determination, thereby improving productivity while managing computational energy consumption efficiently
Data Source
AI summary
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.