REU Site: Software and Data Analytics

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This project will establish a three-year REU site in software and data analytics at East Carolina University (ECU). It will offer a ten-week research program for ten undergraduate students during summer semesters. The faculty-student interaction, as well as interaction among students, will take different forms, including daily Scrum meetings, tutorials, weekly meetings, lectures, seminars, group meetings, and field trips. The REU project will allow a diverse pool of undergraduate students to experience cutting-edge research. Students will gain valuable research skills that will prepare them for their future fields of study, while helping them to develop into self-reliant STEM researchers. Furthermore, their exposure to research will motivate them to continue to graduate studies. Finally, the REU project will provide students with an opportunity to collaborate with their faculty mentors and student peers across the nation after the summer program ends. The sample research projects cover open research topics in software and data analytics. Code Recommendation for Programming Language Learners investigates machine learning techniques for building code recommendation systems aimed at beginning programmers, taking their level of programming knowledge into account. Intelligent Program Update Detection and Automation uses version histories of software systems to understand how code related to uses of a software library (via an Application Programming Interface, or API) evolves, to identify when this evolution needs to occur, and to build transformation scripts to partially or fully automate the changes needed to support a newer API version. Human-Computer Collaborative Dialogue Systems explores techniques for automated regression test case prioritization that utilizes techniques from information retrieval such as term similarity. Link Recovery Systems investigates the use of information retrieval techniques for recovering traceability links between program requirements, bug reports, and project source code. Using Machine Learning to Estimate Software Development Effort explores the use of machine learning techniques to estimate software development effort. Understanding Implicit Extension APIs investigates uses of machine learning for API recommendation, specifically in the context of APIs in dynamic languages that are created implicitly in the code. Machine Learning Algorithms for Biometric Data Analysis uses a combination of machine learning techniques and mobile application usage data (e.g., about swipe gestures) to infer demographic characteristics of app users. Performance Evaluation of Machine Learning Algorithms explores the use of machine learning for prediction, using the example of the next day closing price for crypt-currencies. Students participating in these projects will learn about topics including code recommendation systems, static program analysis, program transformation, classical techniques for classification in machine learning (e.g., k-nearest neighbors), deep learning, information retrieval, software testing, software maintenance, software repository mining, software quality metrics, crypto-currencies, and both theoretical and empirical measurements of algorithm performance.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.