Application Action Alignment for Critical Crash Sequence Detection
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Solution Overview
Problem
Current methods for determining sequences of actions leading to undesirable events in computer applications, such as crashes, are inefficient and inaccurate, requiring manual analysis of vast amounts of test telemetry data.
Innovation Solution
A system and method that aligns and truncates action sequences to identify critical sequences causing predetermined events by inserting gaps for non-matching data, allowing for efficient identification of common actions leading to the event.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual analysis of test telemetry data is used to determine action sequences leading to crashes, then accuracy in identifying critical sequences can be maintained, but productivity is extremely low and investigation time is excessive
Solution Approach 1:
The patent creates fitted action sequence pairs by copying and aligning multiple action sequences, using gap insertion to handle variations. This automated copying and alignment process replaces manual analysis while preserving accuracy in identifying the critical sequence that leads to crashes.
Solution Approach 2:
The patent extracts the critical sequence from numerous action sequences by systematically comparing and aligning them. The fitting alignment process extracts common actions across multiple sequences while removing non-matching data, automatically identifying the critical path without manual intervention.
2Measurement precision
If vast amounts of test telemetry data are collected to ensure comprehensive coverage of action sequences, then measurement precision is improved, but device complexity and data processing burden increase significantly
Solution Approach 1:
The patent extracts only the essential common actions from vast amounts of telemetry data through the fitting alignment process. By focusing on matching actions across sequences and eliminating non-matching data with gaps, the system reduces data processing complexity while maintaining comprehensive coverage.
Solution Approach 2:
The patent segments the analysis into discrete action sequences that can be individually aligned and compared. This segmentation allows the system to handle large volumes of telemetry data in manageable units, reducing overall processing complexity while maintaining comprehensive coverage.
3Adaptability or versatility
If human investigators manually sift through thousands of action lists to derive generalized crash triggers, then adaptability in handling different crash scenarios is maintained, but loss of time is excessive and productivity is poor
Solution Approach 1:
The fitting alignment process is a universal method that can handle different crash scenarios by comparing action sequences regardless of their specific content. The same gap-insertion alignment algorithm works across diverse crash types, providing adaptability without requiring manual adjustment for each scenario.
Solution Approach 2:
The system automatically copies and aligns action sequences from multiple crash instances, deriving generalized crash triggers through systematic comparison. This automated copying and pattern recognition maintains adaptability across different scenarios while eliminating the time-consuming manual review process.
Data Source
AI summary
A system and method to collect an actions list of action sequences in an application leading to a predetermined resulting event, create pairs of the action sequences, apply a fitting alignment to the action sequence pairs to create fitted action sequence pairs, wherein non-matching data between fitted action sequences of each pair is replaced with gaps to ensure that the first and second fitted action sequences are of equal length and are aligned with one another with the gaps being located at index positions the fitted action sequences corresponding to index positions of non-matching data, and delete data, for each of the fitted action sequence pairs, corresponding to the gaps to create a critical sequence of actions for each of the fitted action sequence pairs representing, respectively, common actions of the fitted action sequences of each of the fitted action sequence pairs leading to the predetermined resulting event.