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

Elon University, Elon NC

Investigators

Abstract

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.