Attribute-Based Test Entity Identification in Software Testing
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Solution Overview
Problem
Software testing is hindered by changes in the software environment, as existing test inputs may become invalid, leading to erroneous results and difficulty in identifying and correcting testing configuration issues.
Innovation Solution
A system that collects attribute sets for test entities, analyzes changes in the software environment, and provides an attribute-based view to identify affected test entities, facilitating necessary updates or repairs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated software tests use known software inputs and expected outputs to test correctness, then testing efficiency is improved, but testing accuracy deteriorates when software changes occur because the known inputs may become invalid
Solution Approach 1:
The system performs preliminary actions by collecting attribute sets for test entities before software changes occur, and by proactively identifying affected test entities when changes are detected. This allows the testing system to prepare in advance and adapt quickly when changes happen, maintaining both efficiency and accuracy.
Solution Approach 2:
The system implements feedback mechanisms by monitoring software environment changes and using attribute analysis to identify which test entities are affected. This feedback loop allows the system to automatically update test configurations based on detected changes, ensuring testing accuracy is maintained while preserving automation efficiency.
2Adaptability or versatility
If the software environment changes (e.g., new fields added to GUI), then software functionality is improved, but test entity identification becomes more difficult because existing test inputs may not reflect the changes
Solution Approach 1:
The system continuously monitors software environment changes and provides feedback about which test entities are affected. By analyzing attribute changes and comparing them against stored attribute sets, the system automatically identifies affected test entities without manual intervention, making the process easier despite increased software adaptability.
Solution Approach 2:
The system performs self-service by automatically detecting software changes, analyzing their impact, and identifying affected test entities without requiring manual analysis. The attribute-based view generator and affected test entity identifier enable the system to self-correct and adapt to software changes autonomously.
3Measurement precision
If manual analysis is performed to identify affected test entities after software changes, then testing accuracy is improved, but time consumption increases significantly
Solution Approach 1:
The system performs self-service by automatically identifying affected test entities through attribute analysis rather than requiring manual analysis. The affected test entity identifier and attribute-based view generator enable automated detection and classification of impacted test entities, maintaining high accuracy while eliminating time-consuming manual processes.
Solution Approach 2:
The system replaces manual mechanical analysis with automated computational processes. By using attribute collectors, signal handlers, and automated identification algorithms, the system substitutes human analysis with machine-based processing, achieving both high accuracy and rapid execution.
4Measurement precision
If comprehensive attribute collection is performed for all test entities, then affected test entity identification accuracy is improved, but system complexity increases
Solution Approach 1:
The system applies segmentation by collecting and organizing attributes into structured sets for each test entity, with specific focus on attributes that can indicate changes. The attribute collector and signal handler divide the complex task of monitoring into manageable segments, tracking only relevant attributes that affect test entity identification, thereby maintaining accuracy while managing complexity.
Data Source
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AI summary
An attribute collector may collect an attribute set for each test entity of a plurality of test entities associated with a software test executed in a software environment. An attribute analysis signal handler may receive an attribute analysis signal associated with a change in the software environment, and a view generator may provide an attribute-based view associated with an affected attribute set associated with the change, the attribute-based view identifying an affected test entity that is affected by the change.