Microsoft technology licensing, llc (20240220402). SYSTEMS AND METHODS FOR SELECTING TEST COMBINATIONS OF HARDWARE AND SOFTWARE FEATURES FOR FEATURE VALIDATION simplified abstract
SYSTEMS AND METHODS FOR SELECTING TEST COMBINATIONS OF HARDWARE AND SOFTWARE FEATURES FOR FEATURE VALIDATION
Organization Name
microsoft technology licensing, llc
Inventor(s)
Yingying Chen of Redmond WA (US)
James Lee Wooldridge of Redmond WA (US)
Amitabh Nag of Redmond WA (US)
Josh C. Moore of Redmond WA (US)
Praveen Kuma Arjunan of San Jose CA (US)
SYSTEMS AND METHODS FOR SELECTING TEST COMBINATIONS OF HARDWARE AND SOFTWARE FEATURES FOR FEATURE VALIDATION - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240220402 titled 'SYSTEMS AND METHODS FOR SELECTING TEST COMBINATIONS OF HARDWARE AND SOFTWARE FEATURES FOR FEATURE VALIDATION
- Simplified Explanation:**
The patent application describes systems and methods for selecting test combinations of hardware and software features for product validation. A test scheduler receives metrics for multiple computing entities, uses machine learning algorithms to determine test combinations, and executes these tests for validation.
- Key Features and Innovation:**
- Test scheduler receives metrics for multiple computing entities - Machine learning algorithm used to determine test combinations - Execution of tests for product validation
- Potential Applications:**
- Quality assurance in product development - Testing hardware and software compatibility - Improving product reliability and performance
- Problems Solved:**
- Efficient selection of test combinations - Enhanced product validation process - Reduction of testing time and resources
- Benefits:**
- Improved product quality - Faster time to market - Cost-effective testing process
- Commercial Applications:**
Title: "Optimizing Product Validation with Advanced Testing Systems" Potential commercial uses include: - Technology companies for product development - Quality assurance firms for testing services - Research institutions for experimental validation
- Prior Art:**
Readers can explore prior art related to test combination selection algorithms and machine learning in product validation processes.
- Frequently Updated Research:**
Stay updated on advancements in machine learning algorithms for test combination selection and product validation processes.
- Questions about Product Validation:**
1. How does machine learning improve the efficiency of test combination selection? 2. What are the potential challenges in implementing advanced testing systems for product validation?
Original Abstract Submitted
examples of the present disclosure describe systems and methods for selecting test combinations of hardware and software features for product validation. in examples, a test scheduler of a system receives metrics associated with a first batch of test combinations for multiple computing entities. the metrics may include data associated with a fleet prevalence and a fleet risk determined for the multiple computing entities. the scheduler provides one or more of the metrics to a machine learning algorithm. the test scheduler determines a second batch of test combinations associated with a subset of the multiple computing entities based on an output of the machine learning algorithm. the second batch of combinations may include a subset of the first batch of test combinations. the system executes the second batch of test combinations for the subset of the multiple computing entities.
