Automated Error Analysis via State Rollback and Tool Activation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Diagnosing and characterizing error conditions in computer programs, particularly database applications, is challenging due to transient effects and the need for historical state information, which existing methods fail to address efficiently.
Innovation Solution
Automatically revert the program to a predefined state prior to the error, activate analysis tools, and recommence execution to diagnose the error, generating logs for diagnostic purposes, and revert database changes to a previous state with diagnostic tools activated.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the program is manually analyzed to diagnose error conditions, then diagnostic accuracy can be improved, but time consumption and operational complexity increase significantly
Solution Approach 1:
The system performs preliminary actions by automatically reverting the program to a predefined state before the error occurred and activating analysis tools in advance. This preparation enables comprehensive error analysis to be conducted systematically, improving diagnostic accuracy while reducing the time required for manual investigation
Solution Approach 2:
The system implements self-service by automatically detecting errors, reverting to previous states, activating analysis tools, and generating diagnostic reports without requiring manual intervention. This automation maintains high diagnostic accuracy while significantly reducing time consumption and operational complexity
2Reliability
If analysis tools are activated continuously to monitor program performance, then error detection capability is improved, but system resource consumption increases
Solution Approach 1:
Instead of continuous monitoring, the system employs periodic action by activating analysis tools only when an error is detected. The tools remain dormant during normal operation and are triggered on-demand upon error occurrence, maintaining reliable error detection capability while minimizing system resource consumption
3Loss of information
If historical state information is stored and analyzed to diagnose transient errors, then diagnostic completeness is improved, but data storage requirements and processing complexity increase
Solution Approach 1:
The system extracts only the necessary historical state information from the predefined state before the error occurred, rather than storing and analyzing all possible data. By selectively taking out only the relevant state snapshot needed for diagnosis, the system achieves diagnostic completeness while reducing data storage requirements and processing complexity
Data Source
AI summary
During execution of a program, an error is detected which results in the program reverting to a predefined state prior to the detected error. In some variations, the predefined state is a rollback point. Thereafter, one or more analysis tools are automatically activated that can characterize performance of the program and the execution of the program is recommenced until the error is detected. The analysis tools can then provide information useful for analyzing the cause of the error such as a trace log.