Source Code Log Write Instruction Identification via Token Matching
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Solution Overview
Problem
In large distributed computing systems, identifying the specific log write instruction that generated an event message of interest is extremely time-consuming and error-prone due to the vast number of log write instructions in the source code, often resulting in incorrect identification.
Innovation Solution
The method identifies non-parametric tokens from event messages and calculates confidence scores for candidate log write instructions based on common tokens, ranking them to display potential sources in a graphical user interface.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual searching through source code log write instructions is performed, then complete coverage of all log instructions is achieved, but time consumption and error rate increase significantly
Solution Approach 1:
The patent replaces manual mechanical searching through source code with an automated computer-based system that uses token matching algorithms. The system automatically compares non-parametric tokens from event messages against log write instructions in source code, eliminating the need for manual sifting through thousands of log statements while improving identification accuracy through systematic computational analysis.
Solution Approach 2:
The patent creates token representations (copies) of log write instructions that can be efficiently compared against event messages. By extracting and storing non-parametric tokens from source code log instructions as searchable copies, the system enables rapid matching without requiring analysts to read or manually search the actual source code, significantly reducing search time while maintaining accuracy.
2Productivity
If automated token matching is used to identify log write instructions, then identification speed increases, but complexity of the analysis system increases
Solution Approach 1:
The patent segments the log write instruction matching process into distinct components: token extraction from event messages, token extraction from source code log instructions, token comparison, and result ranking. This segmentation allows each component to be independently optimized and managed, reducing overall system complexity while enabling high-speed automated identification through parallel processing of discrete token operations.
Solution Approach 2:
The patent transforms the complex problem of identifying log write instructions by changing parameters to non-parametric tokens (removing date, time, thread identifiers). This parameter transformation simplifies the matching process by focusing on invariant textual characteristics of log messages, reducing system complexity while dramatically improving identification speed through efficient string-based token comparison.
3Reliability
If all log write instructions are analyzed in detail, then comprehensive coverage is achieved, but computational resources are excessively consumed
Solution Approach 1:
The patent extracts only the essential non-parametric tokens from log write instructions and event messages, separating these key identifying elements from extraneous information such as timestamps, thread IDs, and other variable parameters. This extraction approach maintains identification reliability by focusing on the core textual characteristics while dramatically reducing computational resource consumption by analyzing only the necessary token elements rather than complete log instructions.
Solution Approach 2:
The patent applies partial action by analyzing only the non-parametric token portions of log write instructions rather than performing detailed analysis of entire log statements. This selective partial analysis achieves sufficient identification reliability for most practical purposes while significantly reducing computational resource consumption compared to exhaustive analysis of all log instruction details.
Data Source
AI summary
Methods and systems to identify log write instructions of a source code as potential sources of an event message of interest are described. Methods identify non-parametric tokens, such as text strings and natural language words and phrases, of an event message of interest. Candidate log write instructions and associated line numbers in a source code are identified. Non-parametric tokens of each event message of the one or more candidate log write instructions are determined. A confidence score is calculated for each candidate log write instruction based the number of non-parametric tokens the event message of interest and event message of the candidate log write instruction have in common. The candidate log write instructions are rank ordered based on the corresponding one or more confidence scores and the rank ordered candidate log write instructions and associated line numbers of the source code may be displayed in a graphical user interface.