Software Payload Comparison with Cleansing for Version QA
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional software quality assurance methods struggle with inefficiencies, false positives, and resource-intensive processes during comparisons of software payloads across different versions, particularly in diverse environments like SaaS cloud systems.
Innovation Solution
Agnostic payload processing and real-time quality assurance systems using multithreaded computing infrastructure to compare and cleanse payloads, employing machine learning models to optimize formatting, validation, and sorting, thereby reducing computing resource consumption and improving accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional software quality assurance methods are used to compare software payloads across different versions, then comprehensive testing coverage is achieved, but computing resource consumption increases and false positives occur
Solution Approach 1:
The patent segments the payload comparison process into distinct phases: formatting, cleansing, and comparison. The formatting module processes payloads into standardized formats, the cleansing module removes irrelevant elements, and the comparison module identifies actual differences. This segmentation reduces computing resource consumption by avoiding redundant processing while maintaining comprehensive testing coverage.
Solution Approach 2:
The patent applies preliminary actions by formatting and cleansing payloads before comparison. The formatting module standardizes payload structures in advance, and the cleansing module pre-processes data by removing irrelevant elements such as timestamps and metadata. These preliminary actions reduce the complexity of subsequent comparisons and minimize false positives.
2Measurement precision
If detailed payload comparison is performed to ensure software quality, then testing accuracy improves, but processing time increases
Solution Approach 1:
The patent extracts and removes irrelevant elements from payloads using the cleansing module before comparison. Elements such as timestamps, metadata, and non-essential data are extracted and excluded from the comparison process. This extraction maintains measurement precision by focusing only on relevant payload elements while significantly reducing processing time.
Solution Approach 2:
The patent changes parameters by transforming payloads into standardized formats using the formatting module. This parameter transformation ensures that comparisons are performed on uniformly structured data, improving measurement precision. The standardized format also enables faster processing by eliminating the need for complex format validation during comparison.
3Reliability
If comprehensive payload validation is conducted across diverse software environments, then reliability of quality assurance improves, but system complexity increases
Solution Approach 1:
The patent implements a universal payload processing system that handles diverse software environments through standardized modules. The formatting module universally processes different payload formats into a common structure, the cleansing module universally removes irrelevant elements regardless of source, and the comparison module universally identifies differences. This multi-functionality maintains quality assurance reliability across diverse environments while reducing system complexity through standardized approaches.
Data Source
AI summary
Techniques for software quality assurance are disclosed. A quality assurance system monitors payloads generated by multiple software environments. Responsive to detecting payloads for a particular software environment, the system selects a reference payload generated by a baseline version of the environment and a test payload generated by a modified version of the environment. The system cleanses the selected payloads by identifying elements to be excluded from comparison. The system then determines differences by comparing the test payload to the baseline payload. Based on the comparison, the system generates a report identifying the differences for debugging.