System and method of determining proximity between different populations
A method and system for analyzing a proximity between a first user population and a second user population includes receiving a request to perform a proximity analysis between the first user population and the second user population, accessing data related to the first user population and the second user population, providing the data related to the first user population and the second user population as input to a machine-learning (ML) model for analyzing the data to determine the proximity between the first user population and the second user population, receiving from the ML model as an output at least one of a composite proximity score between the first user population and the second user population, and providing display data relating to the output to a visualization mechanism for display. The composite proximity score may be calculated based on multiple characteristics and/or comparison metrics.
1. A data processing system comprising:
a processor; and
a memory in communication with the processor, the memory comprising executable instructions that, when executed by the processor, cause the data processing system to perform functions of:
receiving a request to perform a proximity analysis between a first user population and a second user population to determine proximity between the first user population and the second user population by determining a proximity between one or more characteristics of the first user population and one or more characteristics of the second user population, each of the one or more characteristics of the first and second user population referring to one attribute of the first and second user populations;
accessing data related to the one or more characteristics of the first user population and the one or more characteristics of the second user population, wherein the data related to the first user population includes at least one of telemetry data associated with testing of a software application and feedback data;
providing the data related the one or more characteristics of the first user population and the one or more characteristics of the second user population as input to a machine-learning (ML) model for analyzing the data to determine the proximity between the first user population and the second user population;
receiving from the ML model as an output at least one proximity score between the first user population and the second user population; and
providing display data relating to the output to a visualization mechanism for display,
wherein:
the ML model is trained, at least in part, by receiving the feedback data labeled via a data labeling element based on the one attribute of the first and second user populations and by utilizing the labeled feedback data to self-learn, and
the trained ML model computes a similarity measure between at least one of the one or more characteristics of the first user population and one of the one or more characteristics of the second user population to determine the proximity between the first user population and the second user population.
2. The data processing system of claim 1 , wherein the instructions further cause the processor to cause the data processing system to perform functions of aggregating the data related to the first user population and the second user population before the data is provided to the ML model.
3. The data processing system of claim 1 , wherein the first user population is a pre-production population and the second user population is a post-production population for testing a software application.
4. The data processing system of claim 3 , wherein the pre-production population is smaller in size than the post-production population.
5. The data processing system of claim 1 , wherein the data related to the second population includes at least one of telemetry data and feedback data.
6. The data processing system of claim 1 , wherein the output includes at least one of one or more characteristics identified as being relevant to a software application for which testing is being performed, and a ranking of the identified characteristics.
7. The data processing system of claim 1 , wherein the visualization mechanism provides a quantitative comparison between the first user population and the second user population in an interactive manner.
8. The data processing system of claim 1 , wherein the instructions further cause the processor to cause the data processing system to perform functions of:
identifying which characteristics of the one or more characteristics of the first user population and the one or more characteristics of the second user population are relevant to testing of the software application;
calculating proximity scores for characteristics identified as being relevant to testing of the software application; and
calculating the proximity score between the first user population and the second user population based on the calculated proximity scores.
9. A method for analyzing a proximity between a first user population and a second user population, comprising:
receiving a request to perform a proximity analysis between a first user population and a second user population to determine proximity between the first user population and the second user population by determining a proximity between one or more characteristics of the first user population and one or more characteristics of the second user population, each of the one or more characteristics of the first and second user population referring to one attribute of the first and second user populations;
accessing data related to the one or more characteristics of the first user population and the one or more characteristics of the second user population, wherein the data related to the first user population includes at least one of telemetry data associated with testing of a software application and feedback data;
providing the data related the one or more characteristics of the first user population and the one or more characteristics of the second user population as input to a machine-learning (ML) model for analyzing the data to determine the proximity between the first user population and the second user population;
receiving from the ML model as an output at least one proximity score between the first user population and the second user population; and
providing display data relating to the output to a visualization mechanism for display,
wherein:
the ML model is trained, at least in part, by receiving the feedback data labeled via a data labeling element based on the one attribute of the first and second user populations and by utilizing the labeled feedback data to self-learn, and
the trained ML model computes a similarity measure between at least one of the one or more characteristics of the first user population and one of the one or more characteristics of the second user population to determine the proximity between the first user population and the second user population.
10. The method of claim 9 , further comprising aggregating the data related to the first user population and the second user population before the data is provided to the ML model.
11. The method of claim 9 , wherein the first user population is a pre-production population and the second user population is a post-production population for testing a software application.
12. The method of claim 11 , wherein the pre-production population is smaller in size than the post-production population.
13. The method of claim 9 , wherein the data related to the second population includes at least one of telemetry data and feedback data.
14. The method of claim 9 , wherein the output includes at least one of one or more characteristics identified as being relevant to a software application for which testing is being performed, and a ranking of the identified characteristics.
15. The method of claim 9 , wherein the visualization mechanism provides a quantitative comparison between the first user population and the second user population in an interactive manner.
16. A non-transitory computer readable medium on which are stored instructions that, when executed, cause a programmable device to:
receive a request to perform a proximity analysis between a first user population and a second user population to determine proximity between the first user population and the second user population by determining a proximity between one or more characteristics of the first and second user population referring to one attribute of the first and second user populations;
access data related to the one or more characteristics of the first user population and the one or more characteristics of the second user population, wherein the data related to the first user population includes at least one of telemetry data associated with testing of a software application and feedback data;
provide the data related the one or more characteristics of the first user population and the one or more characteristics of the second user population as input to a machine-learning (ML) model for analyzing the data to determine the proximity between the first user population and the second user population;
receive from the ML model as an output at least one of a proximity score between the first user population and the second user population; and
provide display data relating to the output to a visualization mechanism for display,
wherein:
the ML model is trained, at least in part, by receiving feedback data labeled via a data labeling element based on the one attribute of the first and second user populations and by utilizing the labeled feedback data to self-learn, and
the trained ML model computes a similarity measure between at least one of the one or more characteristics of the first user population and one of the one or more characteristics of the second user population to determine the proximity between the first user population and the second user population.
17. The non-transitory computer readable medium of claim 16 , wherein the instructions further cause the programmable device to aggregate the data related to the first user population and the second user population before the data is provided to the ML model.
18. The non-transitory computer readable medium of claim 16 , wherein the first user population is a pre-production population and the second user population is a post-production population for testing a software application.
19. The non-transitory computer readable medium of claim 16 , wherein the data related to the second population includes at least one of telemetry data and feedback data.
20. The non-transitory computer readable medium of claim 16 , wherein the output includes at least one of one or more characteristics identified as being relevant to a software application for which testing is being performed, and a ranking of the identified characteristics.
21. The non-transitory computer readable medium of claim 16 , wherein the visualization mechanism provides a quantitative comparison between the first user population and the second user population in an interactive manner.