Multi-Channel Software Testing System with Fallback Execution
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Solution Overview
Problem
Current software testing methodologies, such as manual, automation, AI-based, and crowdsource testing, each have limitations, making it difficult to determine the most effective approach for a particular software application, as they are labor-intensive, costly, error-prone, or dependent on robust training data.
Innovation Solution
A system and method that utilize multiple test case execution channels, including manual, automated, crowdsource, and AI-based channels, to classify and execute test cases based on classification parameters, allowing for a primary and secondary channel selection to optimize testing efficiency and adapt to real-time execution factors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If manual testing is used, then testing can be performed with simple setup, but it is labor intensive and slow time to execute
Solution Approach 1:
The patent segments the testing process into multiple independent channels (manual, automated, crowdsource, AI-based) that can operate simultaneously. Each channel handles specific test cases based on classification, allowing parallel execution and improving overall testing speed while maintaining simplicity where needed.
Solution Approach 2:
The patent creates a universal testing framework that can handle multiple testing methodologies through a single system. The system classifies and routes test cases to appropriate channels (manual, automated, crowdsource, AI-based), making the testing infrastructure multi-functional and adaptable to different testing needs without requiring separate systems for each approach.
2Productivity
If automation testing is used, then time to execute is fast, but it has high upfront costs and high fragility
Solution Approach 1:
The patent applies local quality by matching specific test case characteristics with appropriate testing channels. Not all test cases are automated - the system classifies test cases based on parameters like stability requirements, execution speed needs, and complexity, routing only suitable candidates to automated channels while keeping others in manual or crowdsource channels, thereby reducing overall fragility.
Solution Approach 2:
The patent implements beforehand cushioning by having backup channels ready for each test case. The system identifies primary and secondary execution channels in advance, so if the primary automated channel fails or shows high fragility, the test can seamlessly fallback to an alternative channel (manual, crowdsource, or AI-based), preventing complete test failure.
3Extent of automation
If AI based testing is used, then automation level is high, but it requires extensive training effort and has poor efficacy if not trained appropriately
Solution Approach 1:
The patent applies partial action by using AI-based testing only for test cases where it provides sufficient value, rather than attempting to automate all tests with AI. The classification system identifies which test cases are suitable for AI-based approaches based on parameters like complexity, repetition patterns, and available training data, avoiding unnecessary AI training effort for simple test cases that can be handled by other channels.
Solution Approach 2:
The patent introduces an intermediary classification system that sits between test case definition and execution. This intermediary layer evaluates test cases and determines the most appropriate execution channel, including whether AI-based testing is warranted. This prevents direct deployment of AI testing without proper evaluation, ensuring that AI is only used when the benefits outweigh the training requirements.
4Quantity of substance
If crowdsource testing is used, then cost is low, but documentation requirements are high and fragility is high
Solution Approach 1:
The patent implements preliminary action by preparing and classifying test cases before crowdsource execution. The system pre-processes test cases, categorizes them by complexity and documentation needs, and matches them with appropriate crowdsource tasks. This preliminary classification reduces the documentation burden during actual execution by organizing requirements in advance.
Solution Approach 2:
The patent changes parameters by dynamically adjusting the mix of testing channels based on test case characteristics. For crowdsource testing, the system modifies parameters like task complexity, documentation requirements, and quality thresholds based on the specific test case being assigned, optimizing the balance between cost-effectiveness and quality control.
Data Source
AI summary
Disclosed is a system and method for testing an application using multiple software test case execution channels is disclosed. The system may be configured to receive one or more test cases for testing of the application. The system may further be configured identify a primary test case execution channel and a secondary test case execution channel, corresponding to each of the one or more test cases, from a set of test case execution channel based on one or more classification parameters. The system may further be configured execute the one or more test cases using one of the primary test case execution channel and the secondary test case execution channel for testing the application. In one embodiment, the secondary test case execution channel is used on failure of the primary test case execution channel.