SHF: Small: Program Analysis for Data Science

数据科学是一门将计算与统计学相结合的学科，旨在将原始数据转化为见解。数据分析通常是通过将一系列离散的工具和库组合成数据分析管道来执行的。面对这些工具为研究人员提供的日益增长的速度，我们面临的一个悬而未决的问题是：我们能相信声称的结果吗？随着新研究以越来越高的速度产生，数据科学从业者的数量也在不断增长，目前尚不清楚这个社区是否有足够的资源来验证哪怕是一小部分已发表的研究。这个项目的新颖之处在于，它朝着可信数据分析的目标迈出了一些早期的步骤。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。这个项目对人类的影响在于招募和留住少数族裔学生，让他们有机会从事数据分析研究，并帮助他们为STEM领域的职业生涯做好准备。这个项目的第一个贡献是管理一个数据分析管道的语料库。该语料库将为研究人员提供一个了解从业者所进行活动的窗口。这本身将是对数据分析的一般理解的一个有价值的补充。这个项目的第二个贡献将是一组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，以及捕捉潜在的错误。静态分析技术将用于发现可能存在错误的编码习惯。要解决的技术挑战之一是如何分析不完整的代码。这种挑战来自于这样一个事实，即用于编写数据分析代码的语言通常是动态的，可以随时加载新代码。该奖项反映了美国国家科学基金会的法定使命，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。

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.