Timed Pattern Analysis for Platooning Collaboration Failures
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Solution Overview
Problem
Current technologies lack efficient methods for extracting and analyzing interaction bugs in system-of-systems (SoS), particularly in large complexity systems like smart homes and intelligent transportation systems, due to limitations in processing interaction logs, clustering techniques, and localizing code faults.
Innovation Solution
The proposed solution involves the use of a method called TIME-LCS (Timed Message pattern Extraction-Longest Common Subsequence) for pattern mining, which extracts interaction collaboration failure patterns considering multidimensional and temporal features. This method includes time overlapping clustering to classify patterns and code coverage analysis to localize faults in collaboration protocol codes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing interaction log analysis methods are used, then basic interaction data can be processed, but technical capacity for processing interaction log analysis is insufficient
Solution Approach 1:
The patent segments the interaction log analysis process into multiple specialized modules: pattern extraction module that identifies interaction patterns, clustering module that groups similar failures, and fault localization module that pinpoints code defects. This segmentation allows each module to specialize in one aspect of analysis, improving overall detection accuracy without requiring a single complex system to handle all tasks.
Solution Approach 2:
The patent introduces interaction patterns as an intermediary representation between raw interaction logs and bug detection results. These patterns serve as a mediator that transforms complex log data into structured failure patterns, which then feed into clustering and localization processes. This intermediary layer enables more effective analysis by providing a standardized format for processing.
2Reliability
If traditional clustering techniques are used, then simple data grouping can be performed, but absence of a clustering technique that considers cascading failures limits analysis effectiveness
Solution Approach 1:
The patent implements feedback mechanisms in the clustering process by using extracted interaction patterns to guide the clustering of cascading failures. The system continuously refines clusters based on pattern matching results, where previously identified patterns inform subsequent clustering decisions. This feedback loop improves precision in identifying cascading failure sequences without requiring completely new clustering algorithms.
Solution Approach 2:
The patent performs preliminary pattern extraction and interaction model generation before applying clustering techniques. By pre-processing the interaction logs to extract meaningful patterns and temporal relationships, the system prepares the data in advance for more effective clustering. This preliminary action enables traditional clustering methods to work more effectively on pre-structured data, reducing the need for complex clustering algorithms.
3Productivity
If conventional fault localization methods are used, then general code analysis can be performed, but absence of a technique capable of localizing a code fault from an extracted failure pattern limits debugging efficiency
Solution Approach 1:
The patent replaces manual code inspection and conventional static analysis methods with an automated fault localization technique that uses extracted interaction patterns. The system substitutes pattern-matching algorithms and automated reasoning mechanisms for traditional mechanical debugging processes. This substitution dramatically improves debugging efficiency by automatically pinpointing fault locations based on observed failure patterns rather than requiring manual examination of code.
Solution Approach 2:
The patent implements a self-service fault localization mechanism where the system automatically analyzes its own extracted failure patterns and identifies code faults without external intervention. The fault localization module uses the interaction patterns and clustering results to autonomously determine which code segments are most likely containing bugs, enabling the system to self-diagnose issues and reducing the need for manual debugging efforts.
4Measurement precision
If comprehensive interaction log analysis is performed, then detailed failure patterns can be extracted, but resources required to solve interaction bugs increase
Solution Approach 1:
The patent extracts only the essential and relevant interaction patterns from comprehensive interaction logs, rather than analyzing all log data in detail. The pattern extraction module identifies and extracts key failure patterns, temporal relationships, and critical interaction sequences while filtering out redundant information. This selective extraction maintains high accuracy in failure pattern identification while significantly reducing the computational resources required compared to comprehensive analysis of all log data.
Data Source
AI summary
Disclosed is a method for timed pattern-based collaboration failure analysis in a platooning system-of-systems (SoS). A method for timed pattern-based collaboration failure analysis in a SoS performed by a computer device may include extracting an interaction collaboration failure pattern through a collaboration failure analysis in an interaction model generated from an interaction log executed in the SoS; and localizing a fault of an interaction bug from the extracted interaction collaboration failure pattern.