A Multinomial Naïve Bayesian (MNB) Network to Automatically Recommend Topics for GitHub Repositories
Claudio Di Sipio, Riccardo Rubei, Davide Di Ruscio, Phuong T. Nguyen
Abstract
GitHub has become a precious service for storing and managing software source code. Over the last year, 10M new developers have joined the GitHub community, contributing to more than 44M repositories. In order to help developers increase the reachability of their repositories, in 2017 GitHub introduced the possibility to classify them by means of topics. However, assigning wrong topics to a given repository can compromise the possibility of helping other developers approach it, and thus preventing them from contributing to its development.
Topics & Concepts
Computer scienceMultinomial distributionService (business)Source codeSoftwareReachabilityCode (set theory)World Wide WebOrder (exchange)Software engineeringProgramming languageSet (abstract data type)Theoretical computer scienceEconomicsEconomyFinanceStatisticsMathematicsSoftware Engineering ResearchSoftware System Performance and ReliabilityWeb Data Mining and Analysis