{"id":661824,"date":"2020-05-21T22:00:21","date_gmt":"2020-05-22T05:00:21","guid":{"rendered":"https:\/\/cm-edgetun.pages.dev\/en-us\/research\/?post_type=msr-research-item&#038;p=661824"},"modified":"2020-05-21T22:00:21","modified_gmt":"2020-05-22T05:00:21","slug":"the-state-of-the-ml-universe-10-years-of-artificial-intelligence-machine-learning-software-development-on-github","status":"publish","type":"msr-research-item","link":"https:\/\/cm-edgetun.pages.dev\/en-us\/research\/publication\/the-state-of-the-ml-universe-10-years-of-artificial-intelligence-machine-learning-software-development-on-github\/","title":{"rendered":"The State of the ML-universe: 10 Years of Artificial Intelligence & Machine Learning Software Development on GitHub"},"content":{"rendered":"<p>In the last few years, artificial intelligence (AI) and machine learning (ML) have become ubiquitous terms. These powerful techniques have escaped obscurity in academic communities with the recent onslaught of AI & ML tools, frameworks, and libraries that make these techniques accessible to a wider audience of developers. As a result, applying AI & ML to solve existing and emergent problems is an increasingly popular practice. However, little is known about this domain from the software engineering perspective. Many AI & ML tools and applications are open source, hosted on platforms such as GitHub that provide rich tools for large-scale distributed software development. Despite widespread use and popularity, these repositories have never been examined as a community to identify unique properties, development patterns, and trends. <\/p>\n<p>In this paper, we conducted a large-scale empirical study of AI & ML Tool (700) and Application (4,524) repositories hosted on GitHub to develop such a characterization. While not the only platform hosting AI & ML development, GitHub facilitates collecting a rich data set for each repository with high traceability between issues, commits, pull requests and users. To compare the AI & ML community to the wider population of repositories, we also analyzed a set of 4,101 unrelated repositories. We enhance this characterization with an elaborate study of developer workflow that measures collaboration and autonomy within a repository. We\u2019ve captured key insights of this community\u2019s 10 year history such as it\u2019s primary language (Python) and most popular repositories (Tensorflow, Tesseract). Our findings show the AI & ML community has unique characteristics that should be accounted for in future research.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>In the last few years, artificial intelligence (AI) and machine learning (ML) have become ubiquitous terms. These powerful techniques have escaped obscurity in academic communities with the recent onslaught of AI & ML tools, frameworks, and libraries that make these techniques accessible to a wider audience of developers. As a result, applying AI & ML [&hellip;]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","msr-author-ordering":null,"msr_publishername":"","msr_publisher_other":"","msr_booktitle":"","msr_chapter":"","msr_edition":"","msr_editors":"","msr_how_published":"","msr_isbn":"","msr_issue":"","msr_journal":"","msr_number":"","msr_organization":"","msr_pages_string":"","msr_page_range_start":"","msr_page_range_end":"","msr_series":"","msr_volume":"","msr_copyright":"","msr_conference_name":"Proceedings of the 17th International Conference on Mining Software Repositories 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