Automated Testing Platform Using MDM and Distributed Computing
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Solution Overview
Problem
In complex enterprise management systems, the lack of a unified identification for customers, vendors, and products across different departments leads to inefficiencies and organizational dysfunction, and the frequent changes in business processes result in software bugs that are difficult to detect through traditional testing methods.
Innovation Solution
An automated testing platform that utilizes Master Data Management (MDM) databases to store testing parameters and scenarios, combined with distributed computing methods like Java RMI, allowing for simultaneous testing across various operating systems and environments, thereby reducing testing costs and improving realism.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional testing methods are used, then testing can be performed with simple tools, but software bugs are difficult to detect and testing accuracy is low
Solution Approach 1:
The testing system is segmented into multiple independent components: test scenario management module, test execution module, result analysis module, and MDM integration module. Each module handles specific testing tasks independently, improving detection accuracy while maintaining manageable complexity through modular architecture.
Solution Approach 2:
The patent introduces an intermediary testing platform that connects the MDM system with test execution environments. This intermediary layer translates business requirements into automated test scenarios, enabling accurate bug detection without requiring direct complex integration between all system components.
2Adaptability or versatility
If automated testing is implemented across multiple operating systems and environments, then testing realism and coverage improve, but testing infrastructure complexity and costs increase
Solution Approach 1:
The testing platform is designed with universal components that can operate across multiple operating systems and environments. The test execution module supports various OS platforms through standardized interfaces, and the result analysis module processes test data uniformly regardless of the underlying environment, achieving broad coverage without proportional increases in infrastructure complexity.
Solution Approach 2:
The system uses parameter-driven test scenarios where testing configurations (such as target OS, browser, or environment parameters) can be dynamically changed without modifying the core testing infrastructure. This allows the same automated testing framework to adapt to multiple environments by simply changing test parameters rather than building separate infrastructure for each platform.
3Adaptability or versatility
If frequent business process changes are made, then the system remains adaptable to market needs, but software bugs increase and detection difficulty rises
Solution Approach 1:
The testing system implements dynamic test scenario generation that automatically adapts to business process changes. When business processes are modified, the test scenario management module detects changes and automatically updates or creates corresponding test scenarios, maintaining software reliability despite frequent adaptability changes in the underlying business logic.
Solution Approach 2:
The system incorporates feedback mechanisms where test results automatically inform subsequent testing activities. When bugs are detected or business processes change, the feedback loop triggers automatic updates to test scenarios and re-execution of relevant tests, ensuring that software stability is maintained through continuous validation even as business adaptability requirements evolve.
Data Source
AI summary
An automation testing platform that will enable simultaneous testing of new product code over variety of Operating Systems by calling remote machines. In one embodiment, the system is an SAP master data management based system such as NetWeaver, and the testing platform places important testing information onto the master data database itself. The platform then calls and controls the remote machines using distributed computing methodology such as the Java RMI protocol. The system provides the ability to run automated tests according to different technologies, OS, platforms and codelines, and allows for an automatic test portfolio to be managed from a single test catalog. The results can be represented by a variety of configurable user interface reports. The system has an ability to use legacy automation code, and can report on the quality, reliability and stability of the new product code along various configurable key performance indicators.