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 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.

Abstract: Methods and systems for automated scoring of open-source libraries on their state of reuse in software projects are provided. A system is configured to extract information about the software project, determine whether the extracted information includes information regarding any forked projects, calculate a useful fork reuse score for a forked project, calculate a reuse score in the forked project based on source code attributes including a source code class, create a tree structure for the source code class, identify functions from the tree structure of the source code class, identify similar code sections from the two source code files, calculate a code attributes reuse score based on the similar code sections, calculate a dependent consumption reuse score which indicates how much a function is reused by a dependent class, and calculate a unified reuse score based on the reuse score of the forked project and the dependent consumption reuse score.

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 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: 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: Methods and systems for automated scoring of open-source libraries on their state of reuse in software projects are provided. A system is configured to extract information about the software project, determine whether the extracted information includes information regarding any forked projects, calculate a useful fork reuse score for a forked project, calculate a reuse score in the forked project based on source code attributes including a source code class, create a tree structure for the source code class, identify functions from the tree structure of the source code class, identify similar code sections from the two source code files, calculate a code attributes reuse score based on the similar code sections, calculate a dependent consumption reuse score which indicates how much a function is reused by a dependent class, and calculate a unified reuse score based on the reuse score of the forked project and the dependent consumption reuse score.

Abstract: Systems and methods for automatically scoring ecosystem activity are disclose. The method includes receiving data from repositories and developer forums related to a software project, calculating a popularity score of the software project based on the received data, evaluating forks data of forks of the software project, calculating a project activity score of the software project based on the received data and the evaluated forks data, calculating a defect score, calculating a community score by evaluating posts of the developer forums on the software project, calculating a solution score by evaluating solutions provided as a response to queries on the software project, calculating a sentiment score by aggregating and analyzing identified positive and negative sentiments, calculating a userbase score, and calculating an ecosystem activity score based on one or more individual scores.

Abstract: Systems and methods of scoring quality of open source software documentation are disclosed. In one aspect, a method includes downloading the open source software project code and documentation of the open source software project code, analyzing the open source software project code and the documentation, parsing the open source software project documentation into sections, validating the sections of the documentation with project or stack metrics, assessing a quality of sections of the documentation, assessing a quality of language of the documentation; and computing the quality score of the documentation based on the assessed quality of sections and assessed quality of language.

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: 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 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: 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.