Software Regression Detection Using Clustered Test Results
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Solution Overview
Problem
Detecting performance regressions in software testing is challenging due to the fast-paced nature of software development, variability in test results, and the inefficiency of manual comparisons, which are time-consuming and prone to errors.
Innovation Solution
A computer-implemented method for conducting tests, analyzing results, clustering them, and identifying deviations from expected outcomes to detect regressions, using machine learning techniques like UMAP and HDBSCAN for dimensionality reduction and clustering.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual comparison of test results is used to detect performance regressions, then detection capability is maintained, but time consumption and error rate increase significantly
Solution Approach 1:
The patent replaces manual mechanical comparison with automated machine learning-based analysis. The system uses clustering algorithms (HDBSCAN, UMAP) to automatically analyze test results and identify performance regressions, eliminating the need for manual comparison while maintaining detection accuracy and significantly reducing time consumption.
2Reliability
If comprehensive testing of numerous software versions is conducted, then reliability and functionality coverage improve, but testing efficiency and productivity decrease
Solution Approach 1:
The patent creates clusters of similar test results using machine learning algorithms. By grouping test configurations with similar performance characteristics into clusters, the system can analyze representative samples rather than every individual test case, maintaining comprehensive coverage while improving testing efficiency through pattern recognition and duplication avoidance.
Solution Approach 2:
The system dynamically adjusts testing parameters based on detected performance patterns and regressions. By changing test focus based on identified anomalies and using deviation thresholds to prioritize critical cases, the system maintains thorough reliability checking while improving overall testing productivity through adaptive parameter adjustment.
3Measurement precision
If multiple clustering algorithms are used for test result analysis, then detection accuracy improves, but system complexity increases
Solution Approach 1:
The patent divides the complex analysis task into segments by employing multiple specialized clustering algorithms (HDBSCAN for density-based clustering, UMAP for dimensionality reduction) that each handle specific aspects of the data. This segmentation allows the system to achieve high detection accuracy through complementary algorithms while managing complexity by assigning specific functions to each algorithm rather than using a single monolithic system.
Data Source
AI summary
A method and system for software performance regression testing and reporting. A plurality of tests are conducted on a test configuration. Results of each of the plurality of tests are analyzed to render a validity determination for each of the plurality of tests. Based on the validity determinations, a subsequent test to conduct on the test configuration is determined. The subsequent test is conducted on the test configuration, thereby generating a plurality of test results, and the plurality of test results are clustered. Test results deviating from an expected test result are identified based on a deviation threshold, resulting in at least one regression from the expected test result. The test configuration is retested to confirm the occurrence of the at least one regression.