Test Case Similarity Analysis Platform for Repository Organization
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Solution Overview
Problem
Large test case repositories become congested and disorganized due to the storage of duplicate and overlapping test cases, leading to inefficiencies in testing processes.
Innovation Solution
A similarity analysis platform uses machine learning and feedback techniques to determine similarity scores for test cases, grouping and consolidating them to eliminate redundancies and improve data organization, allowing for efficient processing of requests from user devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If test cases are stored in large repositories to increase test coverage, then testing completeness is improved, but data organization and retrieval efficiency deteriorate due to duplicates and overlaps
Solution Approach 1:
The patent segments test cases into groups based on similarity analysis, organizing them into structured collections with defined relationships. This segmentation allows the system to maintain comprehensive test coverage while enabling efficient retrieval by working with organized groups rather than unstructured individual cases.
Solution Approach 2:
The patent transforms the parameter space by introducing similarity scores and organizational metadata to test cases. By changing how test cases are parameterized and indexed (with similarity metrics and group associations), the system achieves both comprehensive storage and efficient retrieval.
2Measurement precision
If all test cases are stored individually to maintain detail accuracy, then test case specificity is preserved, but memory resources are consumed inefficiently due to duplicates
Solution Approach 1:
The patent merges duplicate or highly similar test cases into grouped structures while preserving the essential specificity of each case through metadata and similarity scoring. This merging reduces memory consumption by eliminating redundant storage of identical or near-identical test case data.
3Ease of operation
If similarity analysis is performed on all test cases to eliminate duplicates, then data organization is improved, but processing time increases due to comprehensive analysis requirements
Solution Approach 1:
The patent performs preliminary similarity analysis and grouping of test cases before they are stored or retrieved. By organizing test cases into similarity groups in advance, the system reduces the processing time required during actual testing operations, as the heavy analysis work has already been completed.
Solution Approach 2:
The system automatically performs similarity analysis and organizational updates without requiring manual intervention. The machine learning model continuously analyzes and reorganizes test cases, making the system self-maintaining and reducing the time burden on users.
Data Source
AI summary
A device may obtain test case information for a set of test cases. The test case information may include test case description information, test case environment information, and/or test case defect information. The device may determine a set of field-level similarity scores by using a set of similarity analysis techniques to analyze a set of test case field groups associated with the test case information. The device may determine a set of overall similarity scores for a set of test case groups by using a machine learning technique to analyze the set of field-level similarity scores. The device may update a data structure that stores the test case information to establish one or more associations between the test case information and the set of overall similarity scores. The device may process a request from a user device using information included in the updated data structure.