{"title":"Large-Scale Analysis of the Co-commit Patterns of the Active Developers in GitHub's Top Repositories","authors":"Eldan Cohen, M. Consens","doi":"10.1145/3196398.3196436","DOIUrl":null,"url":null,"abstract":"GitHub, the largest code hosting site (with 25 million public active repositories and contributions from 6 million active users), provides an unprecedented opportunity to observe the collaboration patterns of software developers. Understanding the patterns behind the social coding phenomena is an active research area where the insights gained can guide the design of better collaboration tools, and can also help to identify and select developer talent. In this paper, we present a large-scale analysis of the co-commit patterns in GitHub. We analyze 10 million commits made by 200 thousand developers to 16 thousand repositories, using 17 of the most popular programming languages over a period of 3 years. Although a large volume of data is included in our study, we pay close attention to the participation criteria for repositories and developers. We select repositories by reputation (based on star ranking), and we introduce the notion of active developer in GitHub (observing that a limited subset of developers is responsible for the vast majority of the commits). Using co-authorship networks, we analyze the co-commit patterns of the active developer network for each programming language. We observe that the active developer networks are less connected and more centralized than the general GitHub developer networks, and that the patterns vary significantly among languages. We compare our results to other collaborative environments (Wikipedia and scientific research networks), and we also describe the evolution of the co-commit patterns over time.","PeriodicalId":6639,"journal":{"name":"2018 IEEE/ACM 15th International Conference on Mining Software Repositories (MSR)","volume":"35 1","pages":"426-436"},"PeriodicalIF":0.0000,"publicationDate":"2018-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"11","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 IEEE/ACM 15th International Conference on Mining Software Repositories (MSR)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3196398.3196436","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 11
Abstract
GitHub, the largest code hosting site (with 25 million public active repositories and contributions from 6 million active users), provides an unprecedented opportunity to observe the collaboration patterns of software developers. Understanding the patterns behind the social coding phenomena is an active research area where the insights gained can guide the design of better collaboration tools, and can also help to identify and select developer talent. In this paper, we present a large-scale analysis of the co-commit patterns in GitHub. We analyze 10 million commits made by 200 thousand developers to 16 thousand repositories, using 17 of the most popular programming languages over a period of 3 years. Although a large volume of data is included in our study, we pay close attention to the participation criteria for repositories and developers. We select repositories by reputation (based on star ranking), and we introduce the notion of active developer in GitHub (observing that a limited subset of developers is responsible for the vast majority of the commits). Using co-authorship networks, we analyze the co-commit patterns of the active developer network for each programming language. We observe that the active developer networks are less connected and more centralized than the general GitHub developer networks, and that the patterns vary significantly among languages. We compare our results to other collaborative environments (Wikipedia and scientific research networks), and we also describe the evolution of the co-commit patterns over time.