AI Log Mask Prediction for Accurate Network Test Diagnosis
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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 testing, failing to utilize historical data effectively.
Innovation Solution
Implementing artificial intelligence-based log mask prediction using deep neural networks (DNNs) and recurrent neural networks (RNNs) to predict suitable log masks for generating software logs that facilitate accurate malfunction diagnosis, conserving resources and time by reducing the need for manual interpretation and multiple iterations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual analysis and interpretation of log masks are used, then flexibility and adaptability are maintained, but resource consumption increases and efficiency decreases
Solution Approach 1:
The patent replaces manual mechanical analysis of log masks with an automated AI-based system using deep neural networks and recurrent neural networks. The system automatically predicts suitable log masks from command logs, eliminating the need for manual interpretation and significantly improving testing efficiency while reducing resource consumption.
Solution Approach 2:
The system enables self-service by automatically generating appropriate log masks without requiring manual intervention. The AI model processes command logs and autonomously determines the suitable log masks, making the testing process self-sufficient and reducing dependency on expert manual analysis.
2Measurement precision
If repeated testing with different log masks is performed, then accurate malfunction diagnosis is achieved, but time consumption and resource wastage increase
Solution Approach 1:
The system performs preliminary action by predicting the suitable log mask before actual testing occurs. The AI model analyzes command logs and determines the appropriate log mask in advance, eliminating the need for repeated testing iterations and significantly reducing the time required for accurate malfunction diagnosis.
Solution Approach 2:
The system utilizes feedback from historical testing data and command logs to improve log mask prediction accuracy. The recurrent neural networks learn from past testing outcomes and adjust predictions accordingly, achieving high diagnostic accuracy without requiring multiple repeated tests.
3Measurement precision
If historical testing data is not utilized, then system complexity remains low, but learning opportunities and prediction accuracy are lost
Solution Approach 1:
The system performs preliminary training using historical testing data before actual deployment. The deep neural networks are pre-trained on extensive datasets of command logs and corresponding successful log masks, enabling accurate predictions without requiring complex real-time processing during actual testing operations.
Solution Approach 2:
The system changes parameters by transforming unstructured command logs into structured training data suitable for neural network processing. The historical data is processed and converted into appropriate input formats, enabling the AI model to learn effective patterns while maintaining manageable system 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.