Expectation Library Encapsulates Annotation Assertions
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Solution Overview
Problem
Existing compliance testing methods for annotation-based standards in JEE environments are cumbersome and prone to errors due to the complexity of interpreting and validating assertions across multiple standards, leading to a long learning curve for developers and limited tolerance for standard updates.
Innovation Solution
A shared expectation computation library is used to encapsulate descriptive assertions from annotation-based standards, converting them into a structured data model for parsing and validation, allowing for efficient compliance testing with reduced dependency on specific standards and improved tolerance for updates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If compliance testing is performed using traditional methods for annotation-based standards, then testing coverage can be achieved, but the process becomes cumbersome and error-prone due to complexity
Solution Approach 1:
The patent introduces an intermediary component (compliance testing system with structured data model) that mediates between the annotation-based standards and the testing process. This intermediary translates complex descriptive assertions into a structured format, reducing testing complexity while maintaining reliability.
Solution Approach 2:
The patent transforms the parameters of assertion representation from unstructured descriptive text to structured data models with defined schemas. This parameter change enables automated parsing and validation, reducing errors while maintaining comprehensive testing coverage.
2Reliability
If developers directly interpret and validate assertions across multiple annotation-based standards, then compliance can be verified, but the learning curve becomes long and difficult
Solution Approach 1:
The compliance testing system acts as an intermediary that handles the complexity of interpreting multiple annotation-based standards. Developers interact with the simplified structured data model rather than directly parsing complex standard assertions, improving ease of use while maintaining verification accuracy.
Solution Approach 2:
The patent creates a simplified copy or representation of the complex assertion structures through structured data models. This copy retains the essential compliance requirements while presenting them in a more accessible format for developers.
3Reliability
If compliance tests are tightly coupled with specific annotation-based standards, then accurate compliance checking is achieved, but tolerance for standard updates becomes limited
Solution Approach 1:
The patent segments the compliance testing system into separate components: the structured data model layer and the specific standard implementation layer. This segmentation allows the core testing framework to remain stable while individual standard implementations can be updated independently, improving adaptability without sacrificing compliance checking accuracy.
Solution Approach 2:
The structured data model serves as a universal interface that can accommodate multiple annotation-based standards. This universal layer enables the system to handle different standards through a common framework, increasing versatility and tolerance for standard updates while maintaining accurate compliance verification.
Data Source
AI summary
A system and method can support compliance tests with annotation based standards using an expectation library. The expectation library can convert one or more descriptive assertions into a structured data model, wherein the one or more descriptive assertions describe one or more annotations. Furthermore, the expectation library can parse a source file that contains the one or more annotations based on the structured data model. Additionally, the expectation library can obtain information related to the one or more annotations of the source file from the structured data model.