View source for 18309570. SYSTEMS AND METHODS FOR SELECTING TEST COMBINATIONS OF HARDWARE AND SOFTWARE FEATURES FOR FEATURE VALIDATION simplified abstract (Microsoft Technology Licensing, LLC)

=SYSTEMS AND METHODS FOR SELECTING TEST COMBINATIONS OF HARDWARE AND SOFTWARE FEATURES FOR FEATURE VALIDATION=


==Organization Name==

[[:Category:Microsoft Technology Licensing, LLC|Microsoft Technology Licensing, LLC]]


[[Category:Microsoft Technology Licensing, LLC]]

==Inventor(s)==

[[:Category:Yingying Chen of Redmond WA (US)|Yingying Chen of Redmond WA (US)]][[Category:Yingying Chen of Redmond WA (US)]]

[[:Category:James Lee Wooldridge of Redmond WA (US)|James Lee Wooldridge of Redmond WA (US)]][[Category:James Lee Wooldridge of Redmond WA (US)]]

[[:Category:Amitabh Nag of Redmond WA (US)|Amitabh Nag of Redmond WA (US)]][[Category:Amitabh Nag of Redmond WA (US)]]

[[:Category:Josh C. Moore of Redmond WA (US)|Josh C. Moore of Redmond WA (US)]][[Category:Josh C. Moore of Redmond WA (US)]]

[[:Category:Praveen Kuma Arjunan of San Jose CA (US)|Praveen Kuma Arjunan of San Jose CA (US)]][[Category: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 18309570 titled 'SYSTEMS AND METHODS FOR SELECTING TEST COMBINATIONS OF HARDWARE AND SOFTWARE FEATURES FOR FEATURE VALIDATION

The present disclosure describes systems and methods for selecting test combinations of hardware and software features for product validation. 

* Test scheduler receives metrics for a first batch of test combinations for multiple computing entities.
* Metrics include fleet prevalence and fleet risk for the computing entities.
* Scheduler provides metrics to a machine learning algorithm.
* Second batch of test combinations is determined based on the output of the machine learning algorithm.
* The system executes the second batch of test combinations for a subset of the computing entities.

### Key Features and Innovation:
* Utilizes machine learning algorithm to optimize test combinations for product validation.
* Considers fleet prevalence and fleet risk for computing entities in test selection process.
* Improves efficiency and accuracy of product validation testing.

### Potential Applications:
* Quality assurance testing in software development.
* Hardware and software compatibility testing.
* Product validation in various industries such as automotive, aerospace, and consumer electronics.

### Problems Solved:
* Streamlines the process of selecting test combinations for product validation.
* Enhances the effectiveness of testing by considering fleet prevalence and risk factors.
* Reduces manual effort and human error in test selection.

### Benefits:
* Improved accuracy and efficiency in product validation.
* Cost savings through optimized test selection process.
* Enhanced reliability of products through comprehensive testing.

### Commercial Applications:
* Software development companies for quality assurance testing.
* Hardware manufacturers for compatibility testing.
* Industries requiring rigorous product validation processes.

### Questions about Test Combination Selection:
1. How does the machine learning algorithm optimize test combinations for product validation?
2. What are the potential implications of considering fleet prevalence and risk in test selection?

### Frequently Updated Research:
Stay updated on advancements in machine learning algorithms for test optimization in product validation processes.


==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.



[[Category:G06F11/36]]
[[Category:CPC_G06F11/3688]]