Software Error Prediction via Log Symptom Scoring
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Solution Overview
Problem
Current software systems lack effective methods for predicting when software failures will occur, leading to costly repairs and downtime due to unexpected errors.
Innovation Solution
A computer-implemented method that analyzes system logs to identify symptoms associated with errors, assigns variable scores based on their timeframe, consistency, and frequency, and provides alerts when these scores exceed a threshold, enabling proactive measures to prevent errors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If software systems operate without predictive error detection, then system simplicity is maintained, but system reliability deteriorates due to unexpected failures and downtime
Solution Approach 1:
The system performs preliminary analysis of log entries to identify symptoms that precede errors. By detecting symptom patterns before actual failures occur, the system enables proactive error prevention through predictive alerts, thereby improving reliability without requiring complex real-time intervention mechanisms
Solution Approach 2:
The error prediction system is segmented into distinct functional modules: log entry reading, symptom identification, variable scoring, threshold comparison, and alert generation. This modular segmentation allows the system to maintain manageable complexity while delivering comprehensive predictive error detection capabilities
2Productivity
If predictive error detection is implemented, then productivity is improved by reducing downtime, but loss of time increases due to additional log analysis processing
Solution Approach 1:
The system applies partial action by focusing analysis only on relevant log entries that contain symptom patterns associated with potential errors. Rather than analyzing all log data equally, the system selectively processes entries that meet specific criteria, reducing overall processing time while maintaining effective error prediction
Solution Approach 2:
The system skips unnecessary processing steps by directly identifying symptom patterns from log entries and transitioning to variable scoring. The streamlined process rushes through intermediate steps that do not contribute to the core prediction function, minimizing time loss while maintaining analysis effectiveness
3Measurement precision
If symptom analysis with variable scoring is implemented, then measurement precision is improved for error prediction, but device complexity increases due to multiple scoring criteria
Solution Approach 1:
The system changes parameters by assigning variable scores to symptoms based on multiple criteria including frequency of occurrence, recency, and severity. These dynamic parameter changes enable precise error prediction by quantifying symptom significance, while the systematic approach to parameter assignment keeps complexity manageable
Data Source
AI summary
A log entry identifies an error in the computer or other system. Another log entry identifies a symptom associated with the system error. The symptom occurs in timeframe prior to the system error and a variable score is determined for the symptom according to the timeframe, the consistency with which the symptom appears along with the error, and the frequency with which the symptom occurs throughout the system log. A new occurrence of the symptom is identified. Whether the symptom's variable score exceeds a threshold value for the symptom is determined. In response to determining that the symptom's variable score exceeds the threshold value, an alert indicating that the associated system error is probable is displayed.