18529639. FEATURE DEPLOYMENT READINESS PREDICTION simplified abstract (Microsoft Technology Licensing, LLC)
Contents
- 1 FEATURE DEPLOYMENT READINESS PREDICTION
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 FEATURE DEPLOYMENT READINESS PREDICTION - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Possible Prior Art
- 1.10 Original Abstract Submitted
FEATURE DEPLOYMENT READINESS PREDICTION
Organization Name
Microsoft Technology Licensing, LLC
Inventor(s)
Connie Qin Yang of Seattle WA (US)
Matthew Scott Rosoff of Seattle WA (US)
Nithin Adapa of Seattle WA (US)
Logan Ringer of Mukilteo WA (US)
Steve Ku Lim of Redmond WA (US)
Xiaoyu Chai of Bellevue WA (US)
FEATURE DEPLOYMENT READINESS PREDICTION - A simplified explanation of the abstract
This abstract first appeared for US patent application 18529639 titled 'FEATURE DEPLOYMENT READINESS PREDICTION
Simplified Explanation
The abstract describes a system and method for generating a predicted quality metric for software based on telemetry data from different groups of devices.
- Telemetry data is received from a first group of devices running the software.
- A quality metric is generated for the software based on the first telemetry data.
- Telemetry data is then received from a second group of devices, different from the first group.
- Covariates impacting the quality metric are identified based on features in the telemetry data from both groups.
- A coarsened exact matching process is performed using the identified covariates to generate a predicted quality metric for the software based on the second group of devices.
Potential Applications
This technology could be applied in software development and testing to predict the quality of software based on telemetry data from different groups of devices.
Problems Solved
This technology helps in predicting the quality of software by analyzing telemetry data from various devices, allowing for targeted improvements and optimizations.
Benefits
The system and method described in the patent application can help software developers and testers in assessing and improving the quality of their software products.
Potential Commercial Applications
- Predictive software quality analysis tool for software development companies
- Quality assurance software for monitoring and optimizing software performance
Possible Prior Art
One possible prior art in this field is the use of machine learning algorithms to analyze telemetry data for predicting software quality.
What are the potential limitations of this technology in real-world applications?
There may be challenges in accurately identifying covariates that impact the quality metric and in performing the coarsened exact matching process effectively.
How does this technology compare to existing methods of predicting software quality?
This technology stands out by utilizing telemetry data from different groups of devices to generate a predicted quality metric, which may provide more comprehensive insights compared to traditional methods.
Original Abstract Submitted
Systems and methods directed to generating a predicted quality metric are provided. Telemetry data may be received from a from a first group of devices executing first software. A quality metric for the first software may be generated based on the first telemetry data. Telemetry data from a second group of devices may be received, where the second group of devices is different from the first group of devices. Covariates impacting the quality metric based on features included in the first telemetry data and the second telemetry data may be identified, and a coarsened exact matching process may be performed utilizing the identified covariates to generate a predicted quality metric for the first software based on the second group of devices.
