TETL System for Multi-Domain Test Data Aggregation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional test management tools lack the ability to provide multi-level reporting across multiple projects and domains, focusing primarily on testing functionalities, and fail to support comprehensive analysis of key performance indicators (KPIs) and business-driven metrics, making it difficult for Quality Assurance Managers to evaluate the quality of IT solutions effectively.
Innovation Solution
A Testing and ETL (Extract, Transform, and Load) system, referred to as TETL, which extracts test data from various test platforms, transforms it, and loads it into a data warehouse, enabling reporting across projects and domains by creating a master schema and generating compatible views, allowing for in-depth analysis and reporting through a data management and analysis application.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If test management tools focus on testing functionalities at micro level, then testing accuracy is improved, but macro level reporting capability deteriorates
Solution Approach 1:
The system segments testing data into multiple levels: micro-level individual SUT test data stored in test management tools, and macro-level aggregated data across multiple SUTs and domains stored in a data warehouse. This segmentation allows the system to maintain detailed testing accuracy at the micro level while providing comprehensive macro level reporting by aggregating data from multiple sources.
Solution Approach 2:
The patent introduces a data warehouse as an intermediary between test management tools and macro level reporting requirements. The data warehouse receives, stores, and aggregates test data from multiple test management tools, enabling macro level analysis without compromising the detailed testing functionality at the micro level.
2Ease of operation
If conventional reporting is used, then simplicity is maintained, but in-depth analysis of KPIs and business metrics deteriorates
Solution Approach 1:
The system performs preliminary data extraction, transformation, and aggregation in the data warehouse before reporting. Business metrics and KPIs are pre-calculated and stored in the data warehouse, so when users request reports, they receive pre-processed, ready-to-analyze data without needing to perform complex manual analysis.
Solution Approach 2:
The patent adds a new dimension to reporting by introducing a data warehouse layer that aggregates data across multiple domains, projects, and time periods. This dimensional expansion enables in-depth analysis of business metrics and KPIs while maintaining simple report generation through standardized queries against the aggregated data.
3Adaptability or versatility
If data is extracted using spreadsheet macros, then flexibility is achieved, but time consumption increases
Solution Approach 1:
The patent replaces manual spreadsheet macro operations with an automated data warehouse system. The data warehouse provides standardized interfaces and pre-defined aggregation logic that automatically extract, transform, and load data from multiple test management tools, eliminating the need for manual macro-based extraction while maintaining flexibility through configurable data extraction parameters.
Data Source
AI summary
A testing and extract, transform and load (TETL) system is operable to interface with test platforms testing hardware or software components of information technology systems. The TETL system can execute extract, transform and load operations to load test data into a data warehouse and facilitates evaluating the test data across projects, entities and domains.