AI Log Mask Prediction for Faster Communications System Testing
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Solution Overview
Problem
Current techniques for analyzing log masks in network and system testing are non-systematic, inefficient, and result in resource wastage due to incorrect analysis and repeated iterations, failing to utilize historical data effectively.
Innovation Solution
Employing artificial intelligence-based log mask prediction using deep neural networks (DNNs) and recurrent neural networks (RNNs) to predict suitable log masks for software log analysis, conserving resources and improving efficiency by reducing the need for manual interpretation and iterative testing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual interpretation and iterative testing are used to analyze log masks, then diagnostic accuracy can be achieved through human expertise, but time consumption and resource usage increase significantly
Solution Approach 1:
The patent replaces manual interpretation (mechanical human analysis) with an automated AI system comprising neural networks and processing circuits. The system automatically analyzes log masks, extracts features, and generates diagnostic results without human intervention, thereby reducing time consumption while maintaining diagnostic accuracy through systematic automated processing.
Solution Approach 2:
The patent implements a self-service diagnostic system where the AI model autonomously analyzes log masks, identifies patterns, and generates diagnostic conclusions without requiring manual interpretation. The system serves itself by automatically processing test data, extracting features, and producing diagnostic results, eliminating the need for continuous human expertise while maintaining high diagnostic accuracy.
2Reliability
If iterative testing with multiple log mask modifications is performed, then comprehensive system testing can be achieved, but resource wastage occurs due to repeated iterations and corrections
Solution Approach 1:
The patent applies preliminary action by pre-training the neural network model with extensive test data and log mask patterns before actual diagnostic operations. The system performs preliminary feature extraction and pattern recognition during the training phase, enabling it to quickly and accurately analyze new log masks without requiring multiple iterative modifications and corrections, thus reducing resource wastage while maintaining comprehensive testing coverage.
Solution Approach 2:
The patent implements feedback mechanisms where the AI system continuously learns from diagnostic results and adjusts its analysis patterns. The system uses feedback from previous diagnostic cases to improve its feature extraction and pattern recognition capabilities, reducing the need for repeated iterative testing and corrections while ensuring comprehensive testing coverage through adaptive learning.
3Productivity
If historical test data is not utilized in log mask analysis, then analysis simplicity is maintained, but diagnostic efficiency decreases due to failure to leverage accumulated knowledge
Solution Approach 1:
The patent performs preliminary action by collecting, storing, and pre-processing historical test data and log masks during a training phase. The neural network model is pre-trained with this historical data to learn patterns and relationships, enabling the system to efficiently diagnose new issues by leveraging accumulated knowledge without adding significant complexity to the operational analysis process.
Solution Approach 2:
The patent uses copying by creating a digital representation of historical diagnostic knowledge within the neural network model. The system copies patterns, features, and relationships from historical data into the trained model, allowing it to efficiently apply learned knowledge to new diagnostic cases without directly processing raw historical data during operation, thus improving efficiency while managing complexity.
Data Source
AI summary
In some implementations, a device may receive training data associated with a set of training command logs and a set of training log masks. The device may generate at least one artificial intelligence model for communications system testing. The device may receive a command log, the command log associated with a first log mask. The device may execute the at least one artificial intelligence model to identify a second log mask for a second set of tests. The device may output information associated with the second log mask for the second set of tests.