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 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: 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 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: 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.