Methods and systems for dynamic search listing ranking of software components

1. A system for automatically generating search list rankings of software components, the system comprising:

one or more processors and memory storing instructions that, when executed by the one or more processors, cause the one or more processors to perform operations comprising:

generating a list of software components in response to a request;

generating a plurality of ranking parameters;

determining a first plurality of weight values correlating with each of the plurality of ranking parameters;

generating an index correlating each of the plurality of software components with each of the plurality of ranking parameters;

parsing web data to populate the index;

determining, via the ranking parameters, a plurality of scores for each of the plurality of software components;

storing the scores on the index;

applying the first plurality of weight values to the plurality of scores on the index; and

generating, for each of the software components, a combined score, wherein the combined score is a combination of each of the plurality of scores that are associated with each of the plurality of software components;

wherein the ranking parameters comprise a popularity score, a release score, a detect and quality score, a support score, a fork and download score, a Q&A score and a security and vulnerability score, wherein:

the release score is a standardized measurement relative to a frequency of updates of a software component;

the popularity score is a standardized measurement relative to a popularity of a software component;

the fork and download score is a standardized measurement relative to a number of downloads associated with a software component over a fixed period of time;

the defect and quality score is a standardized measurement relative to an amount of defects logged in association with a software component; and

the Q&A score is a standardized measurement relative to the amount of activity logged in association with a provision of customer support associated with a software component.

2. The system of claim 1 , wherein the popularity score, the release score, the detect and quality score, the support score, the fork and download score, the Q&A score and the security and vulnerability score are normalized across provider data and third party data by a machine learning model.

3. The system of claim 1 , wherein the fork and download score is determined based on a number of user downloads of a software component across different providers of the software component, the different providers of the software component comprising a plurality of code repositories.

4. The system of claim 1 , wherein the Q&A score is based on a number of questions raised in public forums regarding a software component and a time period elapsed before a service provider provides a response.

5. A system for automatically generating search list rankings of software components, the system comprising:

one or more processors and memory storing instructions that, when executed by the one or more processors, cause the one or more processors to perform operations comprising:

generating a list of software components in response to a request;

generating a plurality of ranking parameters;

determining a first plurality of weight values correlating with each of the plurality of ranking parameters;

generating an index correlating each of the plurality of software components with each of the plurality of ranking parameters;

parsing web data to populate the index;

determining, via the ranking parameters, a plurality of scores for each of the plurality of software components;

storing the scores on the index;

applying the first plurality of weight values to the plurality of scores on the index;

generating, for each of the software components, a combined score, wherein the combined score is a combination of each of the plurality of scores that are associated with each of the plurality of software components;

monitoring one or more test software components;

periodically determining, via the plurality of ranking parameters, a plurality of test scores associated with the one or more test software components;

extracting circumstantial web data regarding the plurality of test scores;

generating training data comprising interpreted correlations between the extracted circumstantial data and the plurality of test scores; and

training a machine learning model using the training data, wherein the trained machine learning model is configured to update the weight values.

6. The system of claim 5 , wherein the circumstantial web data comprises data collected from news media, social media, and economic forecasts.

7. A system for automatically generating search list rankings of software components, the system comprising:

one or more processors and memory storing instructions that, when executed by the one or more processors, cause the one or more processors to perform operations comprising:

generating a list of software components in response to a request;

generating a plurality of ranking parameters;

determining a first plurality of weight values correlating with each of the plurality of ranking parameters;

generating an index correlating each of the plurality of software components with each of the plurality of ranking parameters;

parsing web data to populate the index;

determining, via the ranking parameters, a plurality of scores for each of the plurality of software components;

storing the scores on the index;

applying the first plurality of weight values to the plurality of scores on the index; and

generating, for each of the software components, a combined score, wherein the combined score is a combination of each of the plurality of scores that are associated with each of the plurality of software components;

wherein parsing the web data comprises:

scanning, by a data-crawler, public repositories, cloud providers, Q&A, review sites, vulnerability databases to retrieve information on popularity, releases, forks, quality, support, and security information regarding a software component; and

storing, by the data-crawler, the information to the index, wherein the index is configured to operate as file storage.

8. A method for automatically generating search list rankings of software components, the method comprising:

generating a list of software components in response to a request;

generating a plurality of ranking parameters;

determining a first plurality of weight values correlating with each of the plurality of ranking parameters;

generating an index correlating each of the plurality of software components with each of the plurality of ranking parameters;

parsing web data to populate the index;

determining, via the ranking parameters, a plurality of scores for each of the plurality of software components;

storing the scores on the index;

applying the first plurality of weight values to the plurality of scores on the index;

generating, for each of the software components, a combined score, wherein the combined score is a combination of each of the plurality of scores that are associated with each of the plurality of software components;

monitoring one or more test software components;

periodically determining, via the plurality of ranking parameters, a plurality of test scores associated with the one or more test software components;

extracting circumstantial web data regarding the plurality of test scores;

generating training data comprising interpreted correlations between the extracted circumstantial data and the plurality of test scores; and

training a machine learning model using the training data, wherein the trained machine learning model is configured to update the weight values.

9. The method of claim 8 , wherein the circumstantial web data comprises data collected from news media, social media, and economic forecasts.

10. A method for automatically generating search list rankings of software components, the method comprising:

generating a list of software components in response to a request;

generating a plurality of ranking parameters;

determining a first plurality of weight values correlating with each of the plurality of ranking parameters;

generating an index correlating each of the plurality of software components with each of the plurality of ranking parameters;

parsing web data to populate the index;

determining, via the ranking parameters, a plurality of scores for each of the plurality of software components;

storing the scores on the index;

applying the first plurality of weight values to the plurality of scores on the index; and

generating, for each of the software components, a combined score, wherein the combined score is a combination of each of the plurality of scores that are associated with each of the plurality of software components;

wherein parsing the web data comprises:

scanning, by a data-crawler, public repositories, cloud providers, Q&A, review sites, vulnerability databases to retrieve information on popularity, releases, forks, quality, support, and security information regarding a software component; and

storing, by the data-crawler, the information to the index, wherein the index is configured to operate as file storage.

11. One or more non-transitory computer-readable media storing instructions thereon, wherein the instructions, when executed by one or more processors, cause the one or more processors to:

generate a list of software components in response to a request;

generate a plurality of ranking parameters;

determine a first plurality of weight values correlating with each of the plurality of ranking parameters;

generate an index correlating each of the plurality of software components with each of the plurality of ranking parameters;

parse web data to populate the index;

determine, via the ranking parameters, a plurality of scores for each of the plurality of software components;

store the scores on the index;

apply the first plurality of weight values to the plurality of scores on the index; and

generate, for each of the software components, a combined score, wherein the combined score is a combination of each of the plurality of scores that are associated with each of the plurality of software components;

wherein the ranking parameters comprise a popularity score, a release score, a detect and quality score, a support score, a fork and download score, a Q&A score and a security and vulnerability score, wherein:

the release score is a standardized measurement relative to a frequency of updates of a software component;

the popularity score is a standardized measurement relative to a popularity of a software component;

the fork and download score is a standardized measurement relative to a number of downloads associated with a software component over a fixed period of time;

the defect and quality score is a standardized measurement relative to an amount of defects logged in association with a software component; and

the Q&A score is a standardized measurement relative to the amount of activity logged in association with a provision of customer support associated with a software component.

12. The non-transitory computer-readable media of claim 11 , wherein the popularity score, the release score, the detect and quality score, the support score, the fork and download score, the Q&A score and the security and vulnerability score are normalized across provider data and third party data by a machine learning model.

13. The non-transitory computer-readable media of claim 11 , wherein the fork and download score is determined based on a number of user downloads of a software component across different providers of the software component, the different providers of the software component comprising a plurality of code repositories.

14. The non-transitory computer-readable media of claim 11 , wherein the Q&A score is based on a number of questions raised in public forums regarding a software component and a time period elapsed before a service provider provides a response.

15. One or more non-transitory computer-readable media storing instructions thereon, wherein the instructions, when executed by one or more processors, cause the one or more processors to:

generate a list of software components in response to a request;

generate a plurality of ranking parameters;

determine a first plurality of weight values correlating with each of the plurality of ranking parameters;

generate an index correlating each of the plurality of software components with each of the plurality of ranking parameters;

parse web data to populate the index;

determine, via the ranking parameters, a plurality of scores for each of the plurality of software components;

store the scores on the index;

apply the first plurality of weight values to the plurality of scores on the index;

generate, for each of the software components, a combined score, wherein the combined score is a combination of each of the plurality of scores that are associated with each of the plurality of software components;

monitor one or more test software components;

periodically determine, via the plurality of ranking parameters, a plurality of test scores associated with the one or more test software components;

extract circumstantial web data regarding the plurality of test scores;

generate training data comprising interpreted correlations between the extracted circumstantial data and the plurality of test scores; and

train a machine learning model using the training data, wherein the trained machine learning model is configured to update the weight values.

16. The non-transitory computer-readable media of claim 15 , wherein the circumstantial web data comprises data collected from news media, social media, and economic forecasts.

17. The non-transitory computer-readable media of claim 15 , wherein parsing the web data comprises:

scanning, by a data-crawler, public repositories, cloud providers, Q&A, review sites, vulnerability databases to retrieve information on popularity, releases, forks, quality, support, and security information regarding a software component; and

storing, by the data-crawler, the information to the index, wherein the index is configured to operate as file storage.