Exception Analyzer for Automated Error Classification
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Solution Overview
Problem
In complex application systems, identifying and distinguishing between recurring errors and new errors is time-consuming due to the frequent changes in applications and the difficulty in differentiating between environmental and code-related changes, leading to inefficient error resolution processes.
Innovation Solution
A system and method for exception handling and alerting that utilize an analyzer to compare received exceptions with stored data structures, determine new exceptions, and provide alerts based on frequency analysis and priority, enabling efficient error identification and resolution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If exceptions are logged for tracking purposes, then exception tracking capability is improved, but time consumption for reviewing logs increases
Solution Approach 1:
The patent introduces an intermediary system that automatically compares new exceptions against a database of known exceptions. This mediator analyzes exceptions, determines whether they are new or recurring, and generates appropriate alerts without requiring manual log review, thus resolving the contradiction between comprehensive tracking and time-consuming analysis
Solution Approach 2:
The system implements self-service by automatically classifying and analyzing exceptions without human intervention. The exception analysis component autonomously determines whether exceptions are new or recurring by comparing them against stored exception data, eliminating the need for manual log review while maintaining comprehensive tracking
2Adaptability or versatility
If applications are frequently changed, then system adaptability is improved, but difficulty in distinguishing error sources increases
Solution Approach 1:
The patent implements feedback mechanisms where the system continuously monitors exceptions, compares them against known patterns, and provides actionable insights about error sources. This feedback loop enables the system to adapt to frequent changes while maintaining the ability to distinguish between environmental and code-related errors through automated analysis
Solution Approach 2:
The patent replaces manual mechanical analysis of log files with automated computational analysis. The system uses computer-based exception comparison and classification mechanisms to distinguish error sources, eliminating the need for manual log review and providing accurate identification of error origins even in frequently changing applications
3Measurement precision
If manual log review is performed, then detailed exception analysis is improved, but error resolution efficiency decreases
Solution Approach 1:
The system performs self-service by automatically conducting detailed exception analysis through comparison against a database of known exceptions. The exception analysis component autonomously determines whether exceptions are new or recurring, identifies error sources, and generates alerts, providing both detailed analysis and high efficiency without manual intervention
Solution Approach 2:
The patent uses copying by maintaining a database of known exception patterns and stack traces. The system compares new exceptions against these stored copies to quickly determine whether they are new or recurring errors, enabling detailed analysis through pattern matching rather than manual review while maintaining high resolution efficiency
Data Source
AI summary
Methods and system for exception detecting and alerting are described. An exception indicating an occurrence of an error in an application and a path to the exception in an application space may be received. The received exception may be compared to a stored exception in a prior version of an exception data structure to determine whether the received exception is a new exception. The exception data structure may include a plurality of stored exceptions. The results of the comparison may be provided.