ML-Based Test Script Prioritization for Manufacturing Efficiency
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Solution Overview
Problem
Current commodity testing methods are inefficient due to the lack of automated means for continuous learning and script prioritization, leading to excessive testing time and manual intervention, which affects the consistency and accuracy of test script sequencing.
Innovation Solution
An AI-based test script prioritization tool that uses a machine learning model to identify and prioritize test scripts likely to fail or take longer to execute, while skipping less critical scripts, thereby optimizing the testing process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If all test scripts are executed to ensure comprehensive testing quality, then testing reliability is improved, but testing time and productivity deteriorate
Solution Approach 1:
The system uses historical test data to automatically train machine learning models that predict which test scripts are likely to fail, enabling the testing system to self-optimize without manual intervention. The model continuously learns from past test outcomes to improve script prioritization accuracy over time.
Solution Approach 2:
The system dynamically changes the execution parameters of test scripts by prioritizing them based on predicted failure probability and historical performance metrics. Test scripts are re-ranked and re-executed in optimized sequences, changing the traditional static execution order to a dynamic, data-driven sequence that reduces overall testing time while maintaining quality.
2Measurement precision
If manual intervention is used for test script sequencing to maintain accuracy, then testing precision is improved, but automation level and consistency deteriorate
Solution Approach 1:
The system implements continuous feedback loops where test results are automatically captured, analyzed, and used to retrain the machine learning model. Historical test data feeds back into the system to improve future script prioritization, creating a self-improving automated process that maintains or improves accuracy without manual intervention.
Solution Approach 2:
The patent replaces manual mechanical processes of test script sequencing with an automated machine learning-based system. The ML model automatically analyzes test data, predicts failures, and sequences scripts without human intervention, substituting the mechanical manual process with an intelligent automated system that maintains precision while achieving full automation.
3Reliability
If comprehensive test scripts are executed to identify failures, then reliability is improved, but time consumption and productivity worsen
Solution Approach 1:
The system performs preliminary actions by pre-analyzing historical test data and training machine learning models before actual testing begins. The ML model pre-identifies which test scripts are most likely to fail based on historical patterns, allowing the system to prioritize these scripts first and potentially skip less critical ones, thereby reducing overall testing time while maintaining reliable failure detection.
Solution Approach 2:
The system applies partial action by selectively executing only the most critical test scripts that have high predicted failure probability, rather than running all test scripts comprehensively. The ML model identifies a subset of high-value test scripts that provide the most reliable failure detection with minimal time investment, performing just enough testing to ensure quality without excessive execution.
Data Source
AI summary
In product testing, a script prioritization tool (102) is used to intelligently prioritize the execution sequence of test scripts. This tool creates a repository of test outputs from the executions of test scripts and analyzes the outputs to train and deploy a machine learning, ML, model that defines the priority of the scripts that may need to be executed and the scripts whose execution may be skipped without affecting the quality of testing. Scripts that are more likely to fail and/or are time consuming to execute are prioritized, while other scripts may be skipped. The ML model ranks the scripts based on the average execution time of the script, a count of the execution failures of the script, a count of the number of execution retries for the script, and the most recent failure time of the script. The scripts can be executed based on their rankings for efficiency and time-saving.