Shared Expectation Computation Library for Annotation-Based Compliance Testing
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing compliance testing methods for annotation-based standards in JEE environments are inefficient, prone to errors, and require extensive knowledge of multiple standards, leading to inconsistent expectations and redundant computations.
Innovation Solution
A shared expectation computation library encapsulates annotation-based assertions, allowing compliance tests to derive expected values and compare them with actual values, reducing dependency on standards and tolerance for updates, and simplifying the testing process by centralizing expectation logic.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If compliance tests are implemented manually for annotation-based standards, then testing coverage can be achieved, but error rate increases and consistency decreases
Solution Approach 1:
An expectation computation library is introduced as an intermediary component between the annotation-based standards and the compliance tests. This library encapsulates the standard assertions and provides standardized methods for computing expected values, thereby reducing errors and improving consistency without requiring complex manual testing logic in each test case.
Solution Approach 2:
The patent creates a standardized copy of expectation computation logic that can be reused across multiple compliance tests. Instead of manually implementing testing logic for each standard, the same computation library is copied and referenced by different tests, ensuring consistency and reducing errors while maintaining manageable complexity.
2Productivity
If multiple annotation-based standards are tested separately, then comprehensive coverage is achieved, but redundant computations increase and efficiency decreases
Solution Approach 1:
The expectation computation library is designed to be universal and multi-functional, serving multiple compliance tests for different annotation-based standards through a single shared component. This eliminates redundant computations by providing a common foundation that all tests can utilize, thereby improving productivity without sacrificing comprehensive standard coverage.
Solution Approach 2:
The patent merges the expectation computation logic for multiple standards into a single unified library. By combining these functions into one shared resource, the system avoids repeating the same computations across different tests, reducing overall computation time and improving testing efficiency.
3Adaptability or versatility
If compliance tests contain hard-coded expectation logic, then tests can be executed, but adaptability to standard updates decreases and maintenance difficulty increases
Solution Approach 1:
The expectation computation library is designed to be dynamic and adaptable to standard updates. Rather than hard-coding expectations that become obsolete, the library can be updated to reflect new or changed standard assertions, automatically improving adaptability without requiring complete test rewriting.
Solution Approach 2:
The patent segments the compliance testing system into separate modular components: the annotation-based standards, the expectation computation library, and the compliance tests themselves. This segmentation allows the library to be updated independently to accommodate standard changes, improving adaptability while keeping test development straightforward through clear separation of concerns.
Data Source
AI summary
A system and method can implement compliance tests with annotation based standards using a model library. The model library encapsulates expectation computation logic described in one or more annotation based assertions. A compliance test can obtain an actual value for one or more software properties based on evaluating a source file that contains one or more annotations that is defined by the one or more annotation based assertions, and can determine an expected value for the one or more software properties using the model library. Then, the compliance test can compare the actual value for the one or more software properties with the expected value for the one or more software properties.