Function Signature Matching for Duplicate Crash Dump Identification
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for identifying duplicate crash dumps in computer systems are inefficient due to variations in function signatures caused by formatting changes, noise, and other differences, leading to wasted effort in diagnosing and debugging application crashes.
Innovation Solution
A method that combines approximate string matching and exact string matching techniques to generate match scores for function signatures, allowing for the identification of duplicate crash dumps by comparing causal function signatures across a datastore, even with acceptable differences in formatting or noise.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If exact string matching is used to identify duplicate crash dumps, then precision in matching is improved, but the ability to handle formatting variations and noise deteriorates
Solution Approach 1:
The patent transforms the string matching problem from exact character comparison to a parameter-based similarity assessment. By converting function signatures into numerical representations and applying similarity metrics, the system can tolerate formatting variations while maintaining matching precision through configurable threshold parameters.
Solution Approach 2:
The patent introduces an intermediary similarity scoring mechanism between exact string matching and duplicate identification. This intermediary layer computes match scores that bridge the gap between precise matching and tolerance for variations, allowing the system to handle both exact matches and near-matches through a unified scoring framework.
2Adaptability or versatility
If approximate string matching is used to handle formatting variations, then adaptability to different formats is improved, but matching precision deteriorates
Solution Approach 1:
The patent applies partial matching by computing similarity scores for portions of function signatures rather than requiring complete string matches. This allows the system to identify duplicates based on significant portions of matching code while tolerating formatting variations, noise, and minor differences in less critical segments.
Solution Approach 2:
The system uses configurable similarity thresholds as parameters to control the trade-off between adaptability and precision. By adjusting these parameters, the system can be tuned to be more or less strict in its matching criteria, allowing flexible adaptation to different operational requirements.
3Measurement precision
If comprehensive crash dump analysis is performed on all dumps, then diagnostic accuracy is improved, but time consumption and resource usage deteriorate
Solution Approach 1:
The patent performs preliminary filtering by comparing only critical function signatures (such as culprit functions and call stack entries) before conducting comprehensive analysis. This preliminary action identifies candidate duplicate crash dumps that warrant detailed examination, significantly reducing the number of dumps requiring full analysis while maintaining diagnostic accuracy for identified duplicates.
Solution Approach 2:
The patent segments the crash dump analysis into multiple stages: initial signature extraction, similarity scoring, candidate identification, and detailed analysis. This segmentation allows the system to quickly eliminate non-duplicate dumps through fast similarity comparisons while reserving comprehensive analysis only for promising candidates, thereby reducing overall time consumption.
Data Source
AI summary
A method of identifying substantially similar data fields may include accessing a first/second text strings, performing an approximate string-matching algorithm that generates a first score, and comparing the first score to a first threshold. The method also includes performing an exact string-matching algorithm that generates a second score and comparing the second score to a second threshold. The method further includes generating a third score by combining a weighted first score with a weighted second score and comparing the third score to a third threshold. The method also includes determining whether the first text string is substantially similar to the second text string based on the three score comparisons.