Automated Software Upgrade Testing Matrix via Historical Data Analysis
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Solution Overview
Problem
Current methods for software upgrades are inefficient and prone to human error due to the manual process of identifying usage characteristics and testing scope across multiple clients, leading to inaccurate and time-consuming upgrades that fail to account for varied client configurations and usage patterns.
Innovation Solution
An automated process involving data extraction from historical databases to create upgrade matrices, comparing perceived client usage with actual historical data to determine aligned transactions and configurations, thereby refining the testing scope and ensuring accurate and efficient software upgrades.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual processes are used to identify usage characteristics and analyze user responses, then human error and inaccuracy are reduced, but time consumption and labor intensity increase significantly
Solution Approach 1:
The patent replaces manual mechanical processes with automated computer-based systems. Extraction components automatically extract usage characteristics from user responses without human intervention, and processing components automatically analyze the extracted data to determine testing scope, eliminating both human error and time consumption associated with manual analysis
Solution Approach 2:
The system enables self-service automation where the computer system autonomously performs data extraction, analysis, and testing scope determination. The extraction components and processing components work automatically without requiring human operators to manually collect, analyze, or interpret usage data, allowing the system to serve itself in identifying testing requirements
2Measurement precision
If manual querying of users is performed to collect usage data, then data accuracy improves, but productivity and upgrade implementation speed decrease
Solution Approach 1:
The patent replaces manual querying mechanisms with automated data extraction systems. Extraction components automatically retrieve usage characteristics from user responses and databases without manual intervention, while processing components automatically analyze this data to determine testing scope, thereby maintaining data accuracy while dramatically increasing productivity and upgrade implementation speed
Solution Approach 2:
The system performs preliminary automated extraction and analysis of usage characteristics before upgrade implementation. By automatically extracting usage data and determining testing scope in advance, the system prepares upgrade specifications beforehand, enabling faster implementation without sacrificing data accuracy that would require manual verification
3Productivity
If automated extraction processes are implemented to identify usage characteristics, then productivity and speed of upgrade development increase, but system complexity and data processing requirements increase
Solution Approach 1:
The patent divides the automated system into distinct functional segments: extraction components that retrieve usage characteristics, processing components that analyze the extracted data, and output components that generate testing scope specifications. This segmentation manages system complexity by organizing functions into modular, independent units that can be developed and maintained separately while working together to achieve high productivity
Data Source
AI summary
A process for identifying functional and detailed design decisions in a core software architecture across multiple applications and clients includes comparing the results of two independent review sub processes to rapidly build an upgrade testing matrix. The two sub process include an independent functional review and a regression analysis using data extracts from historical data generated from client use of the core software architecture.