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 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.

Abstract: Systems and methods for automatically generating natural language insights for software components from calculated scores are provided. An exemplary method includes identifying the software component uniquely, identifying sources to gather information for the software component, accessing information for the software component from the identified sources, and tabulating the retrieved information. Support summary insights are generated based on the trained catalog of the natural language terms and the tabulated information and indicate a quality of support for the software component. Quality summary insights are generated based on the trained catalog of the natural language terms and the tabulated information and indicate about a quality of the software component. Security summary insights are generated based on the trained catalog of the natural language terms and the tabulated information of the software component and indicate how secure the software component is.

Abstract: Systems and methods for automatically creating a software component reference guide from multiple information sources are disclosed. In one aspect, the method includes receiving, from a user, a request for reference guides for a software components and view corresponding results, identifying the software component, identifying different sources of information for the software component, generating introductory information of the software component, generating technology details of the software component, generating frequently asked questions (FAQs) and their related solutions associated with the software component, training a catalog of natural language terms related to the software components, and providing, based on the trained catalog, the introductory information, technology details, and FAQs to the user.

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 automatically generating natural language documentation for source code files are provided. An exemplary method includes parsing a source code file to extract a function and generate an abstract syntax tree, generating first natural language documentation for each of a plurality of program statements within the function using a programming language neural network model, generating second natural language documentation for the function as a whole by processing a code snippet of the function using a function documentation neural network model, consolidating the first natural language documentation and the second natural language documentation at a function level, a source file level, and a project level to create consolidated natural language documentation, and summarizing multiple sentences of the consolidated natural language documentation into an abstract summary of the source code file by applying a set of rules.