Traffic Capture Diagnostics Using Error Signature Correlation
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Solution Overview
Problem
Conventional Automated Test Equipment (ATE) systems lack the ability to identify root causes of device failure during qualification testing, provide a graphical user interface for controlling test systems, and automatically correlate error patterns with known signatures, leading to inefficient and labor-intensive manual debugging processes.
Innovation Solution
Implement hardware-based traffic capture modules and a graphical user interface that collect and analyze data between a tester and a device under test, correlate error patterns with a known signature database, and provide a user-friendly interface for controlling test flows.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If conventional ATE systems are used for device testing, then testing can be performed, but the systems cannot identify root causes of device failure and require labor-intensive manual debugging
Solution Approach 1:
The system automatically captures traffic data, compares it against a signature database, and identifies root causes without requiring manual intervention. The ATE system serves itself by performing diagnostic functions that would otherwise require human operators to manually analyze logs and identify failures.
Solution Approach 2:
The patent replaces manual mechanical debugging processes with automated electronic systems. Traffic capture modules automatically monitor data flows, and software algorithms automatically compare captured data against known error signatures, substituting human manual analysis with automated computational processes.
2Measurement precision
If manual data log analysis is performed to identify failure causes, then debugging can be conducted, but the process is labor intensive and error prone
Solution Approach 1:
The system implements feedback by automatically comparing captured traffic data against a database of known error signatures. This closed-loop approach provides immediate feedback about potential root causes, allowing the system to automatically identify and report failures with high precision without manual intervention.
Solution Approach 2:
The patent creates a digital copy of the actual traffic data and compares it against copied known error signatures in a database. This copying approach allows automated comparison and pattern recognition, significantly improving both accuracy and efficiency over manual analysis of original logs.
3Adaptability or versatility
If traditional ATE systems are used, then basic testing can be performed, but they lack intelligence to automatically capture and interpret diagnostic information
Solution Approach 1:
The system performs preliminary actions by pre-populating a database with known error signatures and patterns before actual testing begins. During testing, the traffic capture modules automatically compare real-time data against these pre-established patterns, enabling intelligent automatic diagnostic interpretation without adding significant operational complexity.
4Loss of information
If engineers manually collect and analyze data logs, then failure diagnosis can be attempted, but there is not enough information available to determine which logs to analyze
Solution Approach 1:
The traffic capture modules are designed to universally capture multiple types of diagnostic information simultaneously - traffic data, error logs, and system state information. This multi-functional approach ensures comprehensive information availability for all potential failure modes without requiring engineers to manually determine which specific logs to collect.
Data Source
AI summary
A method of identifying error patterns during automated device testing comprises receiving a data pattern from a plurality of capture modules programmed on a programmable logic device, wherein the plurality of capture modules are programmable and operable to selectively capture data traffic to be monitored, and wherein the data traffic comprises a flow of traffic between a DUT and the programmable logic device. The method further comprises comparing the data pattern with known signatures in an error signature database. Also, the method comprises correlating the data pattern with one or more matching known signatures in the error signature database and assigning a score to each of the one or more matching known signatures in the error signature database based a level of correlation.