Integrated Testing System Automating Data Categorization
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Solution Overview
Problem
Conventional testing methods for information technology systems are slow and inefficient, especially when dealing with large amounts of data, necessitating a streamlined approach to identify and utilize frequently used data and associated data types for effective testing.
Innovation Solution
An integrated system that categorizes and stores frequently used data, automatically identifies relevant data types based on project criteria and predefined relationships, and includes additional associated data in the testing process, reducing the need for data duplication and re-testing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional testing methods are used, then testing can be performed, but testing speed and efficiency are slow particularly when dealing with large amounts of data
Solution Approach 1:
The system performs preliminary actions by pre-categorizing and storing frequently used data in a testing system before actual testing occurs. This allows the testing system to have ready-access to needed data, eliminating the need to gather and process large datasets during testing, thereby significantly improving testing speed and efficiency.
Solution Approach 2:
The system extracts and separates the frequently used data from the larger dataset, identifying and isolating only the portions needed for testing. This extraction process allows the system to work with a reduced, manageable subset of data that maintains testing effectiveness while dramatically improving processing speed and efficiency.
2Productivity
If conventional testing methods are used, then testing can be performed, but testing efficiency is low particularly when dealing with large amounts of data
Solution Approach 1:
The system performs preliminary categorization and storage of frequently used data before testing begins. This pre-preparation eliminates time-consuming data gathering and processing activities during actual testing, significantly reducing the time required for data processing and improving overall testing efficiency.
Solution Approach 2:
The system segments the large dataset into categories of frequently used data and other data. By dividing the data into manageable segments and pre-identifying which segments are needed for testing, the system reduces processing time and improves efficiency by focusing only on relevant data portions.
3Ease of operation
If data is manually identified and prepared for testing, then testing can be performed, but the process is time-consuming and labor-intensive
Solution Approach 1:
The system performs self-service by automatically identifying, categorizing, and preparing test data without requiring manual intervention. The system autonomously processes data identification and preparation tasks, eliminating manual labor while reducing time consumption and improving ease of operation.
Solution Approach 2:
The system uses feedback mechanisms to automatically identify frequently used data patterns and adjust data selection accordingly. This feedback-driven approach allows the system to learn from testing requirements and automatically optimize data preparation, reducing manual effort and time requirements.
Data Source
AI summary
Systems, methods, apparatuses, and computer readable media for testing information technology systems and/or applications are provided. In some examples, data may be categorized as frequently used and stored at an information technology system testing system. One or more portions of the frequently used data may then be identified for use in testing an information technology system. The systems, methods, and the like may further include building a testing environment and receiving a test script. In some examples, one or more data types may be identified for use in testing the information technology system based on various project criteria, the received test script, and the like. In some examples, additional data types and data associated therewith may be identified as associated with the one or more identified data types based on a predefined relationship. This additional data may then be automatically included in the testing of the information technology system.