Abstract: Systems and methods for displaying search item scores and related information for easier search result selection. In one aspect, the method includes receiving a search request for a software component, retrieving a programming language information of the software component, retrieving an ecosystem information of the software component, retrieving a licensing information of the software component, retrieving a quality score of the software component, retrieving a security score of the software component, retrieving details of sources and associated details of the software component, dynamically generating a results information widget including information associated with the software component, and searching, based on the retrieved information, internet sources to assimilate associated information of the software components.

Abstract: Systems and methods for automatically detecting search queries from software documents to validate component searches are provided. An example method includes receiving a number of documents, parsing a number of documents to extract a number of sentences, mapping each of the number of sentences to a number of sections, filtering the number of sentences, consolidating the number of sections and the number of filtered sentences into a summary, partitioning the summary into a first number of sentences, replacing one or more of the number of sentences with one or more paraphrase sentences generated by a machine learning model to generate a second number of sentences, and generating a number of search queries based on the second number of sentences.

Abstract: Systems and methods for parsing a software component search query to enable multi entity searches are provided.

Abstract: Systems and methods for automatic validation of software components comparing source code and feature documentation are provided herein. An exemplary method includes assessing functional validation of software components by comparing the claimed features of software components against the features extracted from their source code. To prevent the issue of having to try unverified software components the present disclosure provides a solution that uses machine learning to extract claimed features of a software component and its actual features that are implemented in its source code. Then, for evaluating the software components, the disclosed solution compares the claimed features of software components against the features extracted from their source code to give a validated score to the developer so that the developer can easily decide on choosing the validated software.

Abstract: Systems and methods for automatically generating search list rankings of software components are provided. An exemplary method includes generating a list of software components in response to a request, generating ranking parameters, determining first weight values correlating with each of the ranking parameters, generating an index correlating each of the software components with each of the ranking parameters, parsing web data to populate the index, determining, via the ranking parameters, scores for each of the software components, storing the scores on the index, applying the first weight values to the 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 scores that are associated with each of the software components.

Abstract: Systems and methods for automatically detecting search queries from software documents to validate component searches are provided. An example method includes receiving a number of documents, parsing a number of documents to extract a number of sentences, mapping each of the number of sentences to a number of sections, filtering the number of sentences, consolidating the number of sections and the number of filtered sentences into a summary, partitioning the summary into a first number of sentences, replacing one or more of the number of sentences with one or more paraphrase sentences generated by a machine learning model to generate a second number of sentences, and generating a number of search queries based on the second number of sentences.

Abstract: Systems and methods classifying software components based on multiple information sources are provided. An exemplary method includes retrieving a number of sources including a project documentation file, source code, and dependent project list associated with a software component, extracting a number of entities from the number of sources, processing the number of entities based on a machine learning model, mapping the number of entities to a set of rules, generating a number of categorizations based on the mapping of the number of entities to the set of rules, and ranking the number of categorizations based on the set of rules.

Abstract: Methods and systems for enabling dynamic filters for software search optimization are disclosed. In one aspect, a method includes receiving a search request, user requirements, and the user preferences from the search system, computing one or more filters from a list comprising a programming language filter, a software component license filter, a software component sources filter, a software component support provided filter, a software component type filter, an industry domain filter choices, and a software component security filter, generating a filter widget based on the one or more computed filters, and applying the filter widget crawling internet sources to provide search results.

Abstract: Systems and methods for retrieving and automatically ranking software component search results are provided. An exemplary method includes parsing a search query to extract search entities, assigning each of the search entities a weight value, identifying software component sources based on the search entities, searching the software component sources for software components, retrieving software components, comparing each of the software components with each of the search entities and generating similarity scores based on each comparison, generating match scores by proportionally combining each of the similarity scores with a weight value, mapping the match scores to the software components, generating a combined match score for each of the software components by combining one or more mapped match scores associated with each of the software components, and generating a ranking of the software components based on the combined match scores.

Abstract: Systems and methods for displaying search item scores and related information for easier search result selection.

Abstract: Systems and methods for automatic validation of software components comparing source code and feature documentation are provided herein. An exemplary method includes assessing functional validation of software components by comparing the claimed features of software components against the features extracted from their source code. To prevent the issue of having to try unverified software components the present disclosure provides a solution that uses machine learning to extract claimed features of a software component and its actual features that are implemented in its source code. Then, for evaluating the software components, the disclosed solution compares the claimed features of software components against the features extracted from their source code to give a validated score to the developer so that the developer can easily decide on choosing the validated software.

Abstract: Systems and methods for parsing a software component search query to enable multi entity searches are provided.

Abstract: Systems and methods for automatically generating search list rankings of software components are provided. An exemplary method includes generating a list of software components in response to a request, generating ranking parameters, determining first weight values correlating with each of the ranking parameters, generating an index correlating each of the software components with each of the ranking parameters, parsing web data to populate the index, determining, via the ranking parameters, scores for each of the software components, storing the scores on the index, applying the first weight values to the 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 scores that are associated with each of the software components.