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.

该项目将在东卡罗莱纳大学（ECU）建立一个为期三年的软件和数据分析REU网站。它将在夏季学期为10名本科生提供为期10周的研究计划。教师与学生的互动，以及学生之间的互动，将采取不同的形式，包括日常的会议，辅导，每周会议，讲座，研讨会，小组会议和实地考察。REU项目将允许多样化的本科生体验前沿研究。学生将获得宝贵的研究技能，这将为他们未来的研究领域做好准备，同时帮助他们发展成为自力更生的STEM研究人员。此外，他们接触研究将激励他们继续研究生学习。最后，REU项目将为学生提供一个机会，在暑期课程结束后，与他们的教师导师和全国各地的学生同行合作。样本研究项目涵盖软件和数据分析领域的开放式研究主题。编程语言学习者的代码推荐研究了机器学习技术，用于构建针对初级程序员的代码推荐系统，同时考虑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来理解与软件库的使用相关的代码（经由应用编程接口或API）如何演变，以识别何时需要发生这种演变，并构建转换脚本以部分或完全自动化支持较新的API版本所需的改变。人机协作对话系统探索了自动回归测试用例优先级排序的技术，该技术利用了诸如术语相似性之类的信息检索技术。链接恢复系统研究了信息检索技术的使用，以恢复程序需求，错误报告和项目源代码之间的可追溯性链接。使用机器学习来估计软件开发工作量探索了使用机器学习技术来估计软件开发工作量。了解隐式扩展API研究了机器学习在API推荐中的应用，特别是在代码中隐式创建的动态语言API的上下文中。用于生物特征数据分析的机器学习算法使用机器学习技术和移动的应用使用数据（例如，关于滑动手势）来推断应用用户的人口统计特征。机器学习算法的性能评估探讨了机器学习在预测中的应用，以加密货币的第二天收盘价为例。参与这些项目的学生将学习包括代码推荐系统，静态程序分析，程序转换，机器学习中的经典分类技术（例如，该奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的影响力审查标准进行评估，被认为值得支持。