SHF: Small: Program Analysis for Data Science

SHF：小型：数据科学程序分析

• 批准号: 1910850
• 负责人: Jan Vitek
• 金额: $ 49.97万
• 依托单位: Northeastern University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-10-01 至 2024-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1910850&HistoricalAwards=false
• 关键词: SHF; Small; Program; Analysis; Data

Data Science is a discipline that combines computing with statistics with the aim of turning raw data into insights. Data analysis is typically performed by composing a series of discrete tools and libraries into a data-analysis pipeline. Faced with the increased velocity that these tools afford to researchers, one of the open questions we are facing is: can we trust the claimed results? As new studies are produced at an increasingly high rate, and the number of data-science practitioners keeps growing, it is unclear if the community has the resources to validate even a fraction of the research in print. The novelty of this project is to take some of the early steps towards the goal of trustworthy data analysis. The project's impact is to increase trust in the results of data analysis performed with technologies such as R and Spark. The project will also impact the broader community of R and Spark developers, by offering analysis tools that can be used widely within the community. The human impact of this project is in recruitment and retention of minority students to opportunities in data-analysis research, and in helping them prepare for careers in STEM.The first contribution of this project is to curate a corpus of data-analysis pipelines. This corpus will give researchers a window into the activities performed by practitioners. This, in and of itself, will be a valuable addition to the general understanding of data analytics. The second contribution of this project will be a set of dynamic- and static-analysis tools that will be used to find faults in data-analysis pipelines. Dynamic analysis will be used to gather behavioral data about the programs and libraries as well as catch latent bugs. Static-analysis techniques will be used to find coding idioms that are potentially buggy. One of the technical challenges that will be solved is how to analyze incomplete code. This challenge comes from the fact that the languages used to write data-analysis code are often dynamic and can load new code at any time.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.

数据科学是一门将计算与统计相结合的学科，旨在将原始数据转化为见解。数据分析通常通过将一系列离散的工具和库组合到数据分析管道中来执行。面对这些工具为研究人员提供的更快的速度，我们面临的一个开放性问题是：我们能相信声称的结果吗？随着新研究的产生速度越来越快，数据科学从业者的数量也在不断增长，目前还不清楚社区是否有足够的资源来验证哪怕是一小部分的印刷研究。该项目的新奇在于采取了一些早期步骤，以实现可信数据分析的目标。该项目的影响是增加对使用R和Spark等技术执行的数据分析结果的信任。该项目还将通过提供可在社区内广泛使用的分析工具，影响更广泛的R和Spark开发人员社区。这个项目的人文影响是在招聘和保留少数民族学生的机会，在数据分析研究，并帮助他们准备在STEM的职业生涯。这个项目的第一个贡献是策划数据分析管道语料库。该语料库将为研究人员提供一个了解从业人员活动的窗口。这本身将是对数据分析的一般理解的一个有价值的补充。该项目的第二个贡献是一套动态和静态分析工具，用于发现数据分析管道中的错误。动态分析将用于收集有关程序和库的行为数据，以及捕获潜在的错误。静态分析技术将被用来发现可能存在错误的编码习惯用法。其中一个将要解决的技术挑战是如何分析不完整的代码。这一挑战来自这样一个事实，即用于编写数据分析代码的语言通常是动态的，并且可以随时加载新代码。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

CodeDJ: Reproducible Queries over Large-Scale Software Repositories

CodeDJ：大规模软件存储库的可重复查询

• 发表时间: 2021
• 期刊: European Conference on Object-Oriented Programming (ECOOP
• 作者: Petr Maj, Konrad Siek

signatr: A Data-Driven Fuzzing Tool for R

Signatr：R 数据驱动模糊测试工具

• DOI: 10.1145/3567512.3567530
• 发表时间: 2022
• 期刊: SLE
• 作者: Turcotte, Alexi;Donat-Bouillud, Pierre;Křikava, Filip;Vitek, Jan
• 通讯作者: Vitek, Jan

Designing types for R, empirically

根据经验设计 R 类型

• DOI: 10.1145/3428249
• 发表时间: 2020
• 期刊: Proceedings of the ACM on Programming Languages
• 作者: Turcotte, Alexi;Goel, Aviral;Křikava, Filip;Vitek, Jan
• 通讯作者: Vitek, Jan

Julia's Efficient Algorithm for Subtyping Unions and Covariant Tuples

Julia 用于子类型联合和协变元组的高效算法

• DOI: 10.4230/lipics.ecoop.2019.24
• 发表时间: 2019
• 期刊: European Conference on Object-Oriented Programming (ECOOP
• 作者: Chung, Benjamin;Zappa Nardelli, Francesco;Vitek, Jan
• 通讯作者: Vitek, Jan