Firmware Testing Optimization via Trace Point Analysis
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Solution Overview
Problem
Firmware testing for information handling systems is inefficient due to the vast number of possible states and input combinations, leading to a tradeoff between test coverage and time, exacerbated by scarcity of shared testing resources.
Innovation Solution
The method involves identifying trace points, test cases, and test resources, performing a full regression test to determine optimized test-case, test-resource tuples that achieve maximum coverage or minimum duration, using matrix representations and scoring equations to identify the most efficient testing combinations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If full regression testing is performed on all test cases across all test resources, then test coverage is maximized, but test time becomes excessively long
Solution Approach 1:
The patent applies partial action by performing only a subset of test cases on subsets of test resources based on optimization algorithms. Instead of executing all test cases on all test resources, the system identifies and executes only the most valuable combinations that provide maximum coverage per unit time, thus reducing overall test time while maintaining acceptable coverage levels.
Solution Approach 2:
The patent changes the parameters of test execution by dynamically selecting which test cases run on which test resources based on calculated coverage scores and duration metrics. The optimization process adjusts execution parameters (which tests run, on which resources, in what order) to achieve the best time-efficiency ratio, transforming the static full-regression approach into a dynamic optimized subset approach.
2Productivity
If more test resources are allocated to increase testing throughput, then productivity improves, but resource availability and cost increase
Solution Approach 1:
The patent introduces dynamics into resource allocation by optimizing test-case to test-resource assignments based on real-time or near-real-time metrics. The system dynamically determines which test cases should run on which available resources, maximizing the utilization of existing resources rather than requiring additional static resource allocation. This dynamic optimization achieves higher throughput with the same resource quantity.
Solution Approach 2:
The optimization system serves itself by automatically analyzing test case characteristics, resource capabilities, and performance metrics to make intelligent allocation decisions without requiring manual resource provisioning. The system self-optimizes the allocation of existing resources to achieve maximum productivity, reducing the need for additional resource investment.
3Speed
If test cases are optimized to reduce execution time, then test speed increases, but test coverage may be reduced
Solution Approach 1:
The patent implements feedback mechanisms by calculating and comparing coverage scores for different test-case and test-resource combinations. The optimization process uses feedback from coverage analysis to guide selection of test subsets, ensuring that the chosen subset maintains adequate coverage while achieving faster execution. The system continuously adjusts selections based on coverage feedback to balance speed and reliability.
Data Source
AI summary
Disclosed methods for testing information handling system software identify trace points, test cases, and test resources for testing firmware or another type of software and perform a full regression of the software to obtain test information including coverage information indicative of traces points reached by each test case and test duration information indicative to time required to perform each test case, wherein the full test include performing each test case on each test resource. Based on the test information, optimized test case test resource (TCTR) tuples are determined for efficiently testing the software. The optimized TCTR tuples, when executed, achieve greater coverage per time interval than the full test. Performing the full regression test may include executing each test case on each testing resource.