EAGER: Semantics for Learning Functional Programming

EAGER：学习函数式编程的语义

• 批准号: 1803362
• 负责人: Shriram Krishnamurthi
• 金额: $ 15万
• 依托单位: Brown University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-01-01 至 2021-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1803362&HistoricalAwards=false
• 关键词: EAGER; Semantics; Learning; Functional; Programming

Programming is a rigorous and intellectually demanding activity. Programmers are expected to provide instructions to a black box device, whose behavior is very different from their own, to accomplish complex tasks. Even seasoned professionals can find this challenging, and beginners often struggle to do it. These problems are greatly amplified when a program has errors or produces incorrect output. A major obstacle is for the programmer to understand how the computer works at a level that is useful for expressing their needs and correcting their programs. The intellectual merits are to evaluate existing models of programming systems, and to define new ones that enable programmers to better understand the computer's execution. The project's broader significance and importance are to make effective programming more accessible to a much broader range of people, including those who intend to apply computing in other data-intensive domains.Concretely, the research examines the use of programming language semantics as explanatory tools for non-technical users. Existing semantics provide rich explanations of behavior, but are expressed in highly technical terms that require significant expertise to understand and use. Furthermore, they have not been tested through application to actual debugging tasks. Therefore, this work intends to open up investigation into the human factors aspects of programming language semantics, understanding how well they perform in different settings, and potentially defining new semantics forms that are better suited to a broad range of programmers. The work will specifically focus on functional programming with an eye towards its role in data science curricula, which are of value across a broad spectrum of disciplines.

编程是一项严格和智力要求高的活动。程序员被期望向黑盒设备提供指令，黑盒设备的行为与他们自己的行为非常不同，以完成复杂的任务。即使是经验丰富的专业人员也会发现这是一个挑战，初学者往往很难做到这一点。当程序有错误或产生不正确的输出时，这些问题会被大大放大。一个主要的障碍是程序员理解计算机如何在一个水平上工作，这对表达他们的需求和纠正他们的程序是有用的。智力上的优点是评估现有的编程系统模型，并定义新的模型，使程序员能够更好地理解计算机的执行。该项目的更广泛的意义和重要性是使有效的编程更容易获得更广泛的人，包括那些打算在其他数据密集型domain.Concrete应用计算，研究探讨使用编程语言语义的解释工具，为非技术用户。现有的语义提供了丰富的行为解释，但表达在高度技术性的术语，需要大量的专业知识来理解和使用。此外，它们还没有通过实际调试任务的应用进行测试。因此，这项工作的目的是打开调查的人的因素方面的编程语言语义，了解如何以及他们在不同的设置中执行，并可能定义新的语义形式，更适合于广泛的程序员。这项工作将特别关注函数式编程，着眼于其在数据科学课程中的作用，这些课程在广泛的学科中具有价值。

项目成果

Will Students Write Tests Early Without Coercion?✱

学生会在没有强迫的情况下尽早写测试吗？â±

• DOI: 10.1145/3428029.3428060
• 发表时间: 2020
• 期刊: International Conference on Computing Education Research
• 作者: Wrenn, John;Krishnamurthi, Shriram
• 通讯作者: Krishnamurthi, Shriram

Evaluating the Tracing of Recursion in the Substitution Notional Machine

评估替代概念机中的递归追踪

• DOI: 10.1145/3159450.3159479
• 发表时间: 2018
• 期刊: SIGCSE
• 作者: Tunnell Wilson, Preston;Fisler, Kathi;Krishnamurthi, Shriram
• 通讯作者: Krishnamurthi, Shriram

The Next 700 Semantics: A Research Challenge

接下来的 700 个语义：研究挑战

• DOI: 10.4230/lipics.snapl.2019.9
• 发表时间: 2019
• 期刊: Summit on Advances in Programming Languages
• 作者: Shriram Krishnamurthi, Benjamin S.

Using Relational Problems to Teach Property-Based Testing

使用关系问题来教授基于属性的测试

• DOI: 10.22152/programming-journal.org/2021/5/9
• 发表时间: 2021
• 期刊: The art science and engineering of programming
• 作者: Wrenn, John;Nelson, Tim;and Krishnamurthi, Shriram
• 通讯作者: and Krishnamurthi, Shriram

The behavior of gradual types: a user study

渐进类型的行为：用户研究

• DOI: 10.1145/3276945.3276947
• 发表时间: 2018
• 期刊: Proceedings of the 14th {ACM} {SIGPLAN} International Symposium on Dynamic Languages
• 作者: Tunnell Wilson, Preston;Greenman, Ben;Pombrio, Justin;Krishnamurthi, Shriram
• 通讯作者: Krishnamurthi, Shriram