CRI: RUI: CI-EN: Infrastructure to Enable Mining and Analysis of Open Source Software Engineering Artifacts

这个NSF CRI支持的本科院校研究（RUI）项目将整合、扩展和增强目前由三个研究社区使用的几个不同的数据源：研究自由、自由和开源软件（FLOSS）的人，更大的实证软件工程研究社区，以及从事数据挖掘和文本挖掘的研究人员。该项目将充分整合现有的公共数据与这些研究团体正在使用的其他几个流行的数据源。这种集成和增强的数据公地将继续为本科生和代表性不足的群体直接参与FLOSS和软件工程研究提供宝贵的机会。这些工具将由团队合作开发，并免费授权给其他人使用和构建。该项目最终将通过增加对FLOSS如何制作的研究的数量和质量，以及在FLOSS之外的领域（如商业软件工程和数据/文本挖掘）进行更好的研究，从而使社会受益。研究界也将继续成为共享科学资源的典范。FLOSS的进一步扩展将包括从软件工程过程中收集、存储和管理新的文本工件，增强文本挖掘软件工具套件，特别是在数据共享中搜索和分析这些新工件。新的重点将是使用图形数据库来理解软件开发人员的网络，使用面向文档的数据库，如带有map-reduce的NoSQL键值存储，用于查询非结构化文本。数据/文本挖掘的增强将允许经验软件工程研究人员和数据挖掘者最终能够访问高质量的、非常大的真实通信工件集合，以及分析它们和共享结果的工具。所收集的文本数据将是可供公众使用的半结构化和非结构化文本的一些最大的策划集合，包括以前从未收集过的新数据源。

This NSF CRI supported Research at Undergraduate Institutions (RUI) project will integrate, expand and enhance several distinct data sources currently used by three research communities: those who study Free, Libre, and Open Source Software (FLOSS), the larger empirical software engineering research community, and researchers engaged in data mining and text mining. The project will fully integrate existing data commons with several other popular data sources being used by these research communities. This integrated and enhanced data commons will continue to serve as a valuable opportunity for undergraduate research students and under-represented groups to participate directly in FLOSS and software engineering research. The tools will be collaboratively developed by the team and freely licensed for others to use and build upon. The project will ultimately benefit society by enabling an increased quantity and quality of research on how FLOSS is made, as well as better research in areas beyond FLOSS, such as commercial software engineering and data/text mining. The research community will also continue to be a model for sharing scientific resources.Further expansion of FLOSS will include collecting, storing, and curating new text artifacts from the software engineering process, enhancing the suite of text mining software tools, specifically to search and analyze these new artifacts inside the data commons. The new focus will be on the use of graph databases for understanding networks of software developers, document-oriented databases such as a NoSQL key-value store with map-reduce for querying unstructured text. The data/text mining enhancements will allow empirical software engineering researchers and data miners to finally have access to high-quality, very large collections of real communication artifacts, and the tools to analyze them and share results. The text data collected will be some of the largest curated collections of semi-structured and unstructured text available anywhere for public use, including new data sources never collected before.