Automated Test Log Keyword Extraction for Abnormal Information
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Solution Overview
Problem
Conventional methods for detecting abnormal information during application program testing are inefficient, requiring extensive time to search through large system logs and often leading to omissions, which hinders the improvement of application programs.
Innovation Solution
A method and device that generate a test command line based on user input, execute a test, monitor the test log, and perform keyword identification to extract abnormal information automatically, using modules for test running, identification, and extraction to quickly and accurately isolate abnormal keywords from logs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual searching through system logs is used to determine abnormal information, then the user can identify abnormal keywords, but the time consumption increases significantly and omissions are likely to occur
Solution Approach 1:
The system performs self-service by automatically monitoring test logs, identifying abnormal keywords, and extracting abnormal information without requiring manual intervention. The test running module, identification module, and extraction module work together to autonomously complete the entire process of abnormal information acquisition, eliminating the need for users to manually search through logs.
Solution Approach 2:
The patent replaces the mechanical manual searching process with an automated computer-based system. The identification module uses keyword identification algorithms to automatically detect abnormal information in test logs, substituting the manual mechanical search process with an efficient automated information processing system.
2Loss of information
If manual searching through system logs is used to determine abnormal information, then the user can identify abnormal keywords, but the complexity of the operation increases due to the large volume of log information
Solution Approach 1:
The extraction module specifically extracts abnormal information from the test log by identifying and isolating abnormal keywords. This extraction process separates the relevant abnormal information from the large volume of normal log data, presenting only the critical information to users and eliminating the need to manually navigate through extensive logs.
Solution Approach 2:
The system autonomously performs the complex task of analyzing large volumes of log information, identifying abnormal patterns, and extracting relevant data. This self-service capability handles the operational complexity internally, presenting simplified results to users without requiring them to manually process the complex log data.
3Productivity
If automated keyword identification is implemented, then the time for extracting abnormal information is reduced, but the device complexity increases due to additional modules
Solution Approach 1:
The testing system integrates multiple functions into a unified automated framework. The test running module not only executes tests but also coordinates with the identification and extraction modules. This multi-functional integration allows the system to perform test execution, log monitoring, keyword identification, and information extraction through a coordinated module structure that achieves high productivity while managing complexity through functional integration.
Data Source
AI summary
Devices and methods are provided for acquiring abnormal information. For example, a test command line is generated using one or more data processors based on at least information associated with test demand information; the generated test command line is run using the data processors to send a test instruction to execute a test of a condition associated with the demand information; a test log is monitored using the data processors; keyword identification is performed using the data processors on the test log; and in response to one or more test abnormal keywords existing in the test log based on at least information associated with the keyword identification, abnormal information associated with the abnormal keywords is extracted using the data processors from the test log.