EML Automation Tool for Accurate End-to-End Scenario Testing
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Solution Overview
Problem
Existing test automation tools struggle to efficiently test end-to-end scenarios using Entity Manipulation Language (EML) due to manual and error-prone processes, as they require manual testing of each query and entity, which is time-consuming and prone to errors.
Innovation Solution
An EML automation tool that automatically tests E2E scenarios by statically specifying target business objects, using READ, MODIFY, and COMMIT statements to perform transactional behaviors, and applies machine learning for issue identification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual testing of each query and entity is performed, then testing coverage can be achieved, but time consumption and error rates increase
Solution Approach 1:
The system performs self-testing by automatically executing EML queries and validating results against expected outcomes. The test automation tool independently manages the testing process, eliminating the need for manual intervention in each test case execution while maintaining comprehensive testing coverage.
Solution Approach 2:
Manual testing operations are replaced with an automated test automation tool that executes EML queries programmatically. The mechanical process of manual testing is substituted with an automated software system that can execute, validate, and report test results without human intervention.
2Productivity
If automated test scripts are used for E2E scenarios, then execution efficiency improves, but error detection capability deteriorates
Solution Approach 1:
The test automation tool incorporates feedback mechanisms that automatically compare actual EML query results with expected results. When discrepancies are detected, the system generates detailed error reports and can trigger alerting mechanisms, ensuring that automation does not compromise error detection capability.
Solution Approach 2:
The E2E scenario testing is segmented into discrete test cases, each targeting specific EML queries or business objects. This segmentation allows the automated tool to systematically validate individual components while maintaining overall scenario integrity, improving both efficiency and error detection.
3Adaptability or versatility
If OData protocol is used for data exchange, then system integration is achieved, but interface dependency increases
Solution Approach 1:
The patent extracts the data access logic from protocol-dependent interfaces and formulates it in protocol-agnostic EML queries. By taking out the OData-specific interface layer and replacing it with standard EML syntax, the system reduces interface dependency while maintaining integration capabilities.
Solution Approach 2:
EML queries serve as a universal interface for accessing and manipulating business objects, replacing the need for protocol-specific interfaces like OData. This universal approach allows the same EML syntax to work across different systems and data sources, reducing interface complexity and dependency.
4Device complexity
If no dedicated EML testing tool is available, then tool simplicity is maintained, but testing capability is lost
Solution Approach 1:
The patent introduces an intermediary test automation tool that bridges the gap between existing automation frameworks and EML query testing. This intermediary tool provides specialized EML testing capabilities while integrating with broader test automation ecosystems, adding functionality without overwhelming complexity.
Data Source
AI summary
According to some embodiments, systems and methods are provided including one or more models, each model defining a respective data entity; a memory storing processor-executable program code; and a processing unit to execute the processor-executable program code to cause the system to: receive a request to execute a test executable code for a first model of the one or more models; identify a model framework for the first model; generate the test executable code for the first model based on the identified model framework; generate an output via execution of the test executable code; and report an analysis of the generated output.


