Firmware Test Step Conflict Detection for Safe Case Merging
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Solution Overview
Problem
Existing firmware testing methods face challenges in identifying and resolving conflicts between test steps, which can lead to unreliable test outcomes due to changes in hidden states caused by previous commands affecting later commands.
Innovation Solution
A machine learning-based approach using a state attributes probability model to analyze test case logs, identify conflicts, and determine whether to merge or stop merging test cases based on state attribute probabilities, employing vectorization and discounted observation motivation probabilities to assess command interactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If test cases are merged to improve testing efficiency, then productivity increases, but test reliability deteriorates due to undetected conflicts between test steps
Solution Approach 1:
A machine learning model acts as an intermediary between test case merging and conflict detection. The model receives test step commands as input, processes them through probability calculations, and outputs conflict predictions that guide whether merging should proceed, thus mediating between efficiency gains and reliability preservation
Solution Approach 2:
The system performs preliminary conflict detection using the machine learning model before finalizing test case merging. By calculating state attribute probabilities and identifying potential conflicts in advance, the system prevents unreliable test outcomes while maintaining merging benefits
2Measurement precision
If traditional conflict detection methods are used to ensure test reliability, then measurement precision improves, but device complexity increases due to manual analysis requirements
Solution Approach 1:
The patent replaces manual mechanical analysis of test step conflicts with an automated machine learning system. The model automatically processes test commands, calculates probabilities, and identifies conflicts without human intervention, maintaining detection precision while eliminating operational complexity
Solution Approach 2:
The machine learning model performs self-service by automatically analyzing test step conflicts without requiring external manual analysis. The system feeds test commands into the model, which independently calculates state attribute probabilities and generates conflict predictions, reducing the need for complex manual detection mechanisms
Data Source
AI summary
An information handling system stores a list of commands associated with test cases. The system receives the list of commands associated with the test cases, and provides the list of commands to an input layer of a state attributes probability machine learning model. An output layer of the of the state attributes probability machine learning model outputs a state attributes probability. The state attributes probability identifies an overlap of state attributes of test steps in the test cases. Based on the state attributes probability, the system determines whether the test steps of the test cases are in conflict. In response to the test steps not being in conflict, the system merges the test cases. In response to the test steps being in conflict, the system stops a merging of the test cases.