Log File Processing Module for Automated Error Summarization
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Software developers face difficulties in processing large log files containing warnings, errors, and exceptions due to their vast size, leading to these being often ignored during software testing and production, which hampers timely reaction and quality of testing processes.
Innovation Solution
A log file processing module that dynamically and automatically summarizes log file contents by generating a reduced data file in a human-readable format, utilizing a summarization algorithm that processes each line, creates word tokens, and identifies intersections to condense data, allowing for quick understanding and reducing processing time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If log files are processed manually to review warnings, errors, and exceptions, then developers can identify issues, but the processing time becomes unacceptably long due to millions of lines
Solution Approach 1:
The patent introduces an intermediary processing system that automatically analyzes log files using pattern matching and clustering algorithms. This intermediary layer between the raw log files and developers condenses millions of lines into summarized error patterns, maintaining detection accuracy while reducing review time from days to minutes.
Solution Approach 2:
The system creates condensed copies of log data by extracting and clustering essential error patterns rather than processing every individual log entry. This copying approach generates representative summaries that preserve critical issue information while eliminating redundant details, enabling rapid analysis without losing detection precision.
2Loss of information
If all log file contents are stored and analyzed in detail, then complete information is available, but storage requirements become excessive
Solution Approach 1:
The patent extracts only the essential error-pattern information from millions of log lines by identifying unique error types and their frequencies. This extraction process removes redundant repeated entries while preserving critical error information, reducing data volume from millions of lines to a manageable summary that retains complete diagnostic value.
Solution Approach 2:
The system discards redundant log entries that represent the same error patterns while recovering and preserving the essential error type information. By clustering identical or similar errors and keeping only representative samples with aggregated counts, the system maintains information completeness about error types while dramatically reducing the quantity of stored data.
Data Source
AI summary
Various methods, systems, apparatuses, and media for implementing a log file processing module are disclosed. A database stores a plurality of log files each having one or more lines with keywords for searching common keywords of errors and warnings appearing in testing an application. A receiver receives a log file from the database via a communication network. A processor operatively connected to the receiver and the database via the communication network dynamically and automatically summarizes contents of the log file by generating a reduced data file in a human readable format by automatically executing a summarization algorithm.