FMitF Track I: Formal Methods in Software Support for Sound Experimentation

FMITF Track I：声音实验软件支持的形式化方法

• 批准号: 2220422
• 负责人: Emma Tosch
• 金额: $ 66.1万
• 依托单位: University of Vermont & State Agricultural College
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-10-01 至 2023-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2220422&HistoricalAwards=false
• 关键词: FMitF; Track; Formal; Methods; Software

Experiments are a critical tool for understanding the world. Experimentation is ubiquitous, pervasive, and often necessary to understand the relationship between cause and effect. Every time one learns how to interact with a new phone or try to understand why our cars will not start, one informally experiments. As individuals, people experiment for themselves, but organizations also experiment to understand the effects of products, policies or design decisions for many customers, citizens and users. As the world is increasingly mediated by software through people's phones, smart devices, and social media platforms, one's ability to experiment is both driven and limited by software. Unfortunately, software is not often written with support for experimentation in mind. This severely limits one's ability to understand complex cause and effect relationships, such as the role that a news feed algorithm play on users' understanding of current events. This project will develop tools and techniques for making experimentation in software easier and more transparent, without sacrificing flexibility or correctness. At present experimentation in software has no unified framework; while there are libraries, configuration languages, and services that can provide limited support for experimentation, there are few end-to-end systems in the style of e.g., logging frameworks. Instead, developers must typically write bespoke software, resulting in systems that are (a) tailored to a specific task and (b) disconnected from hypotheses and analyses. This project will produce a high-level domain-specific language (DSL) for experimentation that enforces constraints on hypotheses, analyses, and treatment assignment according to the underlying principles of experimental design. Experiments written in this language will be correct by construction vis a vis consistency of hypotheses and treatment assignments with respect to their identifiable effects. The team will then integrate the experimentation DSL into legacy socio-technical software for education, leveraging existing support for gradual typing to implement an orthogonal type system for experimental interventions. To evaluate these tools and promote continued research around formal methods approaches to the experimentation-analysis pipeline, the team will build a publicly available searchable experiment repository. This project is jointly funded by Formal Methods in the Field and the Established Program to Stimulate Competitive Research (EPSCoR).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.

实验是了解世界的关键工具。实验无处不在，普遍，并且通常是了解因果关系之间关系的必要条件。每当人们学习如何与新手机互动或试图理解为什么我们的汽车不会启动，一个非正式的实验。作为个人，人们为自己进行实验，但组织也试验以了解产品，政策或设计决策对许多客户，公民和用户的影响。随着世界越来越多地通过人们的电话，智能设备和社交媒体平台来介导，一个人的实验能力既可以驱动和受软件的限制。不幸的是，软件通常不经过支持实验的支持。这严重限制了一个人理解复杂的因果关系的能力，例如新闻提要算法对用户对当前事件的理解的作用。该项目将开发工具和技术，以使软件中的实验更轻松，更透明，而无需牺牲灵活性或正确性。目前，软件中的实验没有统一的框架。虽然有库，配置语言和服务可以为实验提供有限的支持，但在例如记录框架的样式中，几乎没有端到端系统。取而代之的是，开发人员通常必须编写定制软件，从而导致（a）针对特定任务量身定制的系统，以及（b）与假设和分析脱节的系统。该项目将根据实验设计的基本原理对实验产生高级特定领域的语言（DSL）进行实验。用这种语言编写的实验将通过施工对假设和治疗作业的可识别效果的一致性进行正确。然后，该团队将将实验DSL集成到传统的社会技术软件中，以进行教育，并利用现有的支持逐渐键入实施正交类型系统进行实验干预措施。为了评估这些工具，并围绕实验 - 分析管道的正式方法方法进行了持续的研究，该团队将建立一个可公开可搜索的实验存储库。该项目由该领域的正式方法和既定的竞争研究计划（EPSCOR）共同资助。该奖项反映了NSF的法定任务，并使用基金会的知识分子优点和更广泛的影响审查标准，认为值得通过评估来获得支持。