REU Site: Software and Data Analytics

REU 网站：软件和数据分析

• 批准号: 2050883
• 负责人: Mohammad Nassehzadeh Tabrizi
• 金额: $ 38.13万
• 依托单位: East Carolina University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-03-01 至 2025-02-28
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2050883&HistoricalAwards=false
• 关键词: REU; Site; Software; Data; Analytics

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.

该项目将在东卡罗来纳大学（ECU）建立一个三年的REU网站。它将在夏季学期期间为十个本科生提供十周的研究计划。教师的互动以及学生之间的互动将采取不同的形式，包括每日Scrum会议，教程，每周会议，讲座，研讨会，小组会议和实地考察。 REU项目将允许各种各样的本科生体验尖端的研究。学生将获得宝贵的研究技能，使他们为未来的学习领域做好准备，同时帮助他们发展成为自力更生的STEM研究人员。此外，他们对研究的暴露将促使他们继续研究生研究。最后，REU项目将为学生提供机会，在夏季计划结束后与全国各地的教职员工合作。示例研究项目涵盖了软件和数据分析方面的开放研究主题。针对编程语言学习者的代码建议研究了旨在旨在启动程序员的构建代码建议系统的机器学习技术，并考虑了他们的编程知识水平。智能程序更新检测和自动化使用软件系统的版本历史记录来了解与软件库（通过应用程序编程接口或API）相关的代码如何发展，以识别何时需要发生此进化，并构建转换脚本以部分或完全自动化支持新的API版本所需的更改。人类计算机协作对话系统探讨了自动回归测试案例的技术优先级，该技术利用信息检索（例如术语相似性）中的技术。链接恢复系统研究了信息检索技术在程序要求，错误报告和项目源代码之间恢复可追溯性链接。使用机器学习来估算软件开发工作，探讨了机器学习技术来估计软件开发工作。理解隐式extension API研究了机器学习对API建议的用途，特别是在代码中隐式创建的动态语言中的API中。生物识别数据分析的机器学习算法使用机器学习技术和移动应用程序使用数据（例如，关于滑动手势）的组合来推断应用程序用户的人口统计学特征。机器学习算法的绩效评估探索了机器学习对预测的使用，以第二天的封闭价格的示例进行了隐式货币。 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法定任务，并被认为是值得通过基金会的智力优点和更广泛影响的审查标准来评估的值得支持的。