AI Test Case Selection for Software Development Efficiency
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current software development life cycles are inefficient due to significant human decision-making processes and compatibility issues across platforms, leading to wasted computing and communication resources in software testing, particularly in handling new software requirements and defects.
Innovation Solution
A testing platform utilizing neural network and artificial intelligence models to automatically select and execute test cases, trained with historical software data to predict optimal test cases and configurations, thereby conserving resources and improving efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If manual human decision-making processes are used to select and execute test cases, then flexibility and adaptability in handling complex software requirements are improved, but productivity and resource efficiency deteriorate due to significant time consumption and wasted computing resources
Solution Approach 1:
The system enables self-service by allowing the software development platform to automatically select and execute appropriate test cases through integrated AI models, eliminating the need for manual human decision-making in test case selection while maintaining adaptability to different software requirements and defects
Solution Approach 2:
The patent replaces the mechanical human decision-making process with an automated system that uses machine learning models and historical software data to intelligently select test cases, thereby improving productivity while preserving the adaptability needed for complex software requirements
2Reliability
If comprehensive manual testing of all test cases is performed to ensure software quality, then reliability is improved, but loss of time and computing resources worsen due to exhaustive testing requirements
Solution Approach 1:
The system extracts and prioritizes only the most relevant test cases from the complete test suite by analyzing historical software data and using AI models to predict which tests are most likely to detect defects, thereby maintaining software quality reliability while significantly reducing the time required for comprehensive testing
Solution Approach 2:
Instead of performing exhaustive testing of all possible test cases, the system applies partial action by selectively executing only the subset of test cases that are predicted to be most valuable for detecting defects in the specific software context, thus balancing reliability with time efficiency
3Adaptability or versatility
If traditional software testing methods are used across multiple platforms, then compatibility testing is improved, but device complexity and resource consumption worsen due to handling compatibility issues across different platforms
Solution Approach 1:
The system achieves universality by integrating platform compatibility checking directly into the test case selection process, allowing the same AI-driven selection mechanism to handle multiple platforms simultaneously without requiring separate complex testing systems for each platform
Data Source
Figure 1A
Figure 1B
Figure 1C
AI summary
A device may receive historical software data associated with a software development platform, and may train a neural network model, with the historical software data, to generate a trained neural network model. The device may receive, from the software development platform, historical test configuration data, and may train an execution model, with the historical test configuration data, to generate a trained execution model. The device may receive software data identifying software to be tested, and may process the software data, with the trained neural network model, to predict a set of test cases to execute for testing the software. The device may process the set of test cases, with the trained execution model, to identify configurations, scripts, and test targets for executing the set of test cases, and may perform one or more actions based on the set of test cases, the configurations, the scripts, and the test targets.