Test Suite Optimization via Similarity Index Computation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Software testing is hindered by redundant test cases that contain similar steps, leading to inefficiencies and increased testing time due to conventional methods' inability to effectively identify and eliminate redundant scenarios.
Innovation Solution
A method and system for optimizing test suites by calculating similarity index scores between test cases and identifying cases with scores above a predetermined threshold, allowing for the elimination or modification of redundant test cases to create unique test scenarios, thereby reducing redundancy and improving testing efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple test cases with similar test scenarios are used to ensure comprehensive testing coverage, then testing completeness is improved, but testing time and complexity increase due to redundancy
Solution Approach 1:
The patent merges similar test cases by computing similarity indices between test case scenarios and combining those with high similarity into single optimized test cases. This reduces the total number of test cases while maintaining comprehensive testing coverage, thereby decreasing testing time without sacrificing reliability.
Solution Approach 2:
The patent introduces similarity index computation as a new parameter to identify and eliminate redundant test cases. By calculating similarity metrics between test scenarios and applying threshold-based filtering, the system optimizes the test suite to remove duplicates while preserving essential test coverage.
2Reliability
If multiple test cases with similar test scenarios are executed separately to ensure all scenarios are tested, then testing thoroughness is improved, but device complexity increases due to redundant test execution
Solution Approach 1:
The patent merges test cases with similar scenarios by computing similarity indices and combining redundant cases into single optimized test cases. This reduces test suite complexity while maintaining thoroughness by ensuring that unique test scenarios are preserved and executed.
Solution Approach 2:
The patent applies similarity index computation and threshold-based identification to transform the test suite structure. By changing the parameter of test case selection from exhaustive execution to similarity-based optimization, the system reduces complexity while preserving testing thoroughness.
3Ease of operation
If conventional black box testing with minimal paths is used to simplify testing, then ease of operation is improved, but redundancy in test scenarios cannot be identified or eliminated
Solution Approach 1:
The patent introduces similarity index computation as an intermediary mechanism between simple black box testing and redundancy elimination. This intermediary analysis layer enables automatic identification of redundant test scenarios without complicating the testing approach, maintaining ease of operation while recovering information about redundancy.
Solution Approach 2:
The patent replaces manual redundancy identification with automated similarity-based computation. By substituting mechanical/manual analysis with algorithmic similarity index calculation, the system maintains operational simplicity while enabling automatic detection and elimination of redundant test cases.
4Measurement precision
If test cases are prioritized based on user inputs or requirements to select the most accurate test case, then testing accuracy is improved, but the process becomes time consuming and requires multiple steps
Solution Approach 1:
The patent performs preliminary similarity analysis on all test cases before execution to identify and eliminate redundant scenarios in advance. This preliminary action reduces the test suite to only essential unique test cases, improving accuracy by focusing on distinctive scenarios while reducing selection time through pre-computed similarity metrics.
Solution Approach 2:
The patent changes the selection parameter from multi-step user-based prioritization to automated similarity index computation. By transforming the selection criterion into a computable similarity metric with threshold-based filtering, the system achieves accurate test case selection more efficiently without requiring multiple manual steps.
Data Source
AI summary
The present disclosure relates to a method for optimizing test suite comprising plurality of test cases. The method comprises receiving, a test suite comprising a plurality of test cases along with one or more optimization parameters from one or more sources. The method further comprises computing similarity index scores of each test case by comparing test case scenario of each test case of the plurality of test cases with a first reference test case scenario. The method further comprises identifying first set of one or more test cases among the plurality of test cases requiring optimization when the similarity index scores of the one or more test cases is equal to or more than a predetermined threshold score. The method further comprises performing one or more events on the identified first set of one or more test cases for optimizing the test suite.