Automated Software Test Adaptation via Version Deviation Analysis
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Solution Overview
Problem
Automated software testing is hindered when tests created for older software versions encounter changes in the user interface or functionality, leading to errors and manual intervention, as existing methods struggle to adapt to code changes and missing elements in newer versions.
Innovation Solution
A method utilizing machine learning to analyze deviations between software versions, classify errors, and generate adaptation specifications to automatically update test cases, enabling self-recovery and adaptation of software tests by identifying and addressing code changes and missing elements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If software tests are created for older versions, then test coverage is established, but test reliability deteriorates when software changes occur
Solution Approach 1:
The patent implements dynamic test adaptation by automatically detecting software changes through code comparison and dynamically adjusting test cases based on detected modifications. The system transitions from static test scripts to adaptive test execution that responds to software evolution, maintaining reliability while accommodating changes in user interface elements, functions, and code structure.
Solution Approach 2:
The system employs feedback mechanisms by analyzing test execution results and error specifications to identify failures caused by software changes. This feedback loop enables automatic generation of adaptation specifications that modify test cases, creating a closed-loop system where test reliability is continuously maintained through detection of changes and automated adjustment of test scripts.
2Measurement precision
If manual intervention is used to identify test errors, then accuracy is improved, but productivity deteriorates
Solution Approach 1:
The patent implements self-service automation where the system automatically detects software changes, analyzes error specifications, generates adaptation specifications, and modifies test cases without human intervention. The automated system serves itself by comparing code versions, identifying failed test steps, and performing adaptations, thereby maintaining high accuracy while dramatically improving productivity through elimination of manual error identification and test update processes.
Solution Approach 2:
The system replaces manual mechanical processes of error identification and test adaptation with automated computational mechanisms. Machine learning models and algorithmic processes substitute human analysts, automatically processing deviation specifications and error data to generate adaptations, thereby achieving both high accuracy through systematic analysis and high productivity through automation.
3Productivity
If automated test adaptation is implemented, then productivity is improved, but device complexity increases
Solution Approach 1:
The patent segments the complex test adaptation process into distinct modular components: code comparison modules that detect changes, error analysis modules that process failure specifications, adaptation generation modules that create modification specifications, and test execution modules that apply changes. This segmentation manages system complexity by organizing functions into independent, manageable units while maintaining high automated productivity through coordinated operation of these modules.
Solution Approach 2:
The system employs universal multi-functional components that handle multiple aspects of test adaptation. The code comparison mechanism serves both deviation detection and change classification functions, while the adaptation generation system produces both immediate test corrections and documentation. This universality reduces overall system complexity by using versatile components rather than specialized dedicated systems for each function.
4Loss of time
If test steps are automatically adapted, then loss of time is reduced, but manufacturing precision deteriorates
Solution Approach 1:
The system performs preliminary actions by proactively detecting software changes before test failures occur and pre-generating adaptation specifications. The code comparison and change detection mechanisms operate continuously to identify modifications early, allowing the system to prepare test adaptations in advance, thereby reducing time loss while maintaining precision through systematic analysis of changes before they impact test execution.
Solution Approach 2:
The system uses feedback from test execution results and error specifications to verify adaptation precision. By analyzing failed test steps and comparing them against detected software changes, the system validates that generated adaptations are accurate and appropriate. This feedback loop ensures that automated adaptations maintain manufacturing precision by confirming correctness before and after test modifications.
Data Source
AI summary
A method for automated adaptation of software tests of a software. The method includes ascertaining a deviation specification which indicates a difference between at least two versions of the software; ascertaining an error specification about an error that occurred during an execution of the software test of the software; carrying out an evaluation of the deviation specification and the error specification with respect to a correlation of the error that has occurred and the difference between the versions of the software; generating an adaptation specification based on the carried out evaluation, wherein the adaptation specification specifies at least one item of information for adapting the software test to eliminate the error.