Abstract: Evolutionary learning techniques are used to validate and prioritize open source software libraries for subsequently determining the best open source software library for a specified technical project. Data associated with the open source software candidates is collected into a cluster and, at an eligibility layer, a fitness score is determined for each of the open source software candidate. Candidates that are determined to meet a required fitness score threshold are passed to the crossover layer, at which, software and hardware standards rules are applied to the open source software metadata to validate the open source software. Invalid candidates are held in queue and subjected to rework analysis. A mutation layer executes the crossover layer iteratively until a predetermined volume of open source candidates results. A ranking layer provides a prioritized ranking list, based on the fitness score, of those open source software candidates that have validated.

Abstract: Evolutionary learning techniques are used to validate and prioritize open source software libraries for subsequently determining the best open source software library for a specified technical project. Data associated with the open source software candidates is collected into a cluster and, at an eligibility layer, a fitness score is determined for each of the open source software candidate. Candidates that are determined to meet a required fitness score threshold are passed to the crossover layer, at which, software and hardware standards rules are applied to the open source software metadata to validate the open source software. Invalid candidates are held in queue and subjected to rework analysis. A mutation layer executes the crossover layer iteratively until a predetermined volume of open source candidates results. A ranking layer provides a prioritized ranking list, based on the fitness score, of those open source software candidates that have validated.