Automated Failure Classification via Key Event Similarity
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Solution Overview
Problem
Manual classification of software failures is time-consuming and inaccurate due to the complexity of large software systems, where the same failure phenomenon can occur across different components, making it difficult to determine the responsible component.
Innovation Solution
A method and apparatus that automatically classify failures by identifying key events in logs, determining similarity with historical failures, and classifying based on these similarities, using a processor and memory to process logs and historical data for efficient and accurate categorization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual classification of failures is performed, then human expertise and judgment can be applied, but it consumes a large amount of time and energy and results in low accuracy
Solution Approach 1:
The patent replaces the manual mechanical classification process with an automated computer-based system that uses algorithms to analyze failure logs and determine failure types, thereby eliminating time consumption and human limitations while maintaining or improving accuracy
Solution Approach 2:
The system creates a digital model of failure classification by copying and analyzing patterns from historical failure logs, using these copied patterns to automatically classify new failures without requiring manual human analysis each time
2Measurement precision
If manual analysis of failure phenomenon is performed, then human analysts can use their knowledge, but it is difficult to determine the responsible component due to the same failure phenomenon occurring on different components
Solution Approach 1:
The patent extracts and analyzes specific key information from failure logs such as error codes, stack traces, and component identifiers, separating these critical elements from the complex overall system to enable precise identification of the responsible component
Solution Approach 2:
The system adds a new dimension of analysis by examining multiple attributes and characteristics of failures simultaneously (log patterns, component metadata, historical data) rather than relying on a single perspective, enabling accurate component identification in complex systems
3Productivity
If automated classification is implemented, then speed and consistency are improved, but the system requires processing and comparison of large amounts of historical data
Solution Approach 1:
The system performs preliminary organization and indexing of historical failure logs in advance, structuring the data so that when a new failure occurs, the classification can be performed quickly by comparing against the pre-organized historical data rather than processing everything from scratch
Data Source
AI summary
Embodiments of the present disclosure provide a method and apparatus for classifying a failure. The method comprises obtaining a log associated with a failure and identifying a key event in the log, and the key event indicates a predetermined situation. The method further comprises determining a similarity between the failure and each of a set of historical failures based on the key event, and the set of historical failures have been classified into at least one category. The method further comprises classifying the failure based at least partly on the similarity. Embodiments of the present disclosure enable classifying the failure fast and accurately by determining the similarity associated with key event between failures.