Automated Data Structuring via Machine Learning for Software Testing
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Solution Overview
Problem
Current business processes, such as software testing and analysis, rely heavily on manual human intervention, which is time-consuming and ineffective in achieving key insights for improving business processes and validating software application performance.
Innovation Solution
The implementation of a computer-implemented method using a machine-learning engine to analyze log files and datasets, determining analytical rules to generate structured datasets that represent information flow in transaction processes, thereby automating data structuring and software testing processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual human workers are used to execute computing processes and test software programs, then the processes can be performed with human judgment and adaptability, but the processes become time-consuming and ineffective at achieving key insights
Solution Approach 1:
The system enables automated self-testing by having the computing system itself execute test functions and validate results without human intervention. The automated testing framework allows the system to autonomously perform regression testing, generate test results, and identify defects, replacing manual human execution while maintaining reliability and reducing time consumption.
Solution Approach 2:
The patent replaces the mechanical human action of manually executing test processes with an automated computing-based testing system. The automated testing framework uses software agents and scripts to simulate user interactions, execute test cases, and validate software functions, substituting human manual operations with automated computational processes that are faster and more consistent.
2Measurement precision
If manual resources are used for system knowledge and learning insights, then human expertise can be applied, but the processes are time-consuming and ineffective at validating performance
Solution Approach 1:
The automated testing framework enables continuous validation of software performance through regression testing. The system continuously executes test suites, validates performance metrics, and updates test results without interruption, providing ongoing performance validation that is both precise and high-throughput, unlike manual periodic assessments.
Solution Approach 2:
The system creates automated copies of manual testing processes by replicating user interactions and test scenarios through software agents. These automated test copies execute predefined test cases that mirror manual testing procedures, enabling precise performance validation at scale without requiring human expertise for each individual test execution.
3Productivity
If automated testing frameworks are implemented, then productivity and throughput increase, but the initial setup and configuration become more complex
Solution Approach 1:
The automated testing framework is divided into modular components including test case definitions, execution engines, result validation modules, and reporting systems. Each component operates independently and can be configured separately, reducing overall system complexity while maintaining high productivity. The segmented architecture allows for easier maintenance and scalability.
Solution Approach 2:
The testing framework is designed as a universal system that can validate multiple software functions and performance metrics through a single integrated platform. The automated testing engine supports various test types (functional, performance, regression) and can adapt to different software applications, reducing the need for multiple specialized testing systems and simplifying the overall complexity.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for accessing a database comprising multiple datasets. Each dataset includes data derived from a respective application. A machine-learning engine determines an analytical rule using at least one dataset of the multiple datasets. The analytical rule is determined by processing input data obtained from the at least one dataset derived from the respective application. A structured dataset is generated based on the determined analytical rule. The structured dataset is generated in response to using the determined analytical rule to analyze data from each dataset of the multiple datasets derived from the respective application. One or more data sequences that represent information flow of a transaction process are determined based on the structured dataset.