CAREER: Algorithmic Foundations and Modern Applications for Program Synthesis

职业：程序综合的算法基础和现代应用

• 批准号: 1652140
• 负责人: Aws Albarghouthi
• 金额: $ 45万
• 依托单位: University of Wisconsin-Madison
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-07-01 至 2024-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1652140&HistoricalAwards=false
• 关键词: CAREER; Algorithmic; Foundations; Modern; Applications

Almost every aspect of our world has been touched by automation through software systems. Being able to efficiently construct complex software provides a competitive advantage to businesses, streamlines our bureaucratic systems, and improves our personal day-to-day lives. But building software remains a cumbersome, error-prone process. This project simplifies the software development process through techniques that automatically synthesize desired programs. At a foundational level, the project develops modern algorithmic techniques for efficiently and automatically constructing software systems, thus advancing the field of program synthesis. The particular emphasis of this project is on automated synthesis of data-analysis applications. Specifically, the project develops tools for end users to automatically construct data analyses that can run on cloud infrastructure, without any necessary knowledge of programming systems. Thus, the project has the potential to revolutionize and democratize data analytics. The project strongly connects research with education and outreach: by involving women and underrepresented minority undergraduate students in research, incorporating program synthesis ideas in modern curricula, and developing inclusive synthesis-themed hackathons.This project expands the range of program synthesis applications to data-parallel programs that can run on cloud infrastructure. To enable efficient synthesis, the project contributes a portfolio of novel algorithmic techniques. Specifically, the project (1) develops and utilizes the notion of relational specifications to guide synthesis algorithms with semantic knowledge; (2) develops techniques for learning relational specifications of arbitrary APIs; and (3) develops a suite of techniques for efficiently synthesizing programs that are deterministic in the presence of reorderings.

通过软件系统实现自动化几乎触及了我们世界的方方面面。能够高效地构建复杂的软件为企业提供了竞争优势，简化了我们的官僚系统，并改善了我们的个人日常生活。但构建软件仍然是一个繁琐、容易出错的过程。该项目通过自动合成所需程序的技术简化了软件开发过程。在基础层面上，该项目开发了用于高效和自动构建软件系统的现代算法技术，从而推动了程序综合领域的发展。该项目的重点是数据分析应用程序的自动合成。具体地说，该项目为最终用户开发工具，以自动构建可在云基础设施上运行的数据分析，而无需任何必要的编程系统知识。因此，该项目有可能使数据分析发生革命性变化并使之大众化。该项目将研究与教育和外联紧密联系在一起：让女性和代表不足的少数族裔本科生参与研究，在现代课程中融入程序合成想法，并开发包容性合成主题黑客松。该项目将程序合成应用程序的范围扩展到可以在云基础设施上运行的数据并行程序。为了实现高效的合成，该项目贡献了一系列新颖的算法技术。具体地说，该项目(1)开发并利用关系规范的概念来指导具有语义知识的合成算法；(2)开发用于学习任意API的关系规范的技术；以及(3)开发一套在存在重新排序的情况下高效地合成确定性程序的技术。

项目成果

Proving data-poisoning robustness in decision trees

• DOI: 10.1145/3385412.3385975
• 发表时间: 2020-06
• 期刊: Proceedings of the 41st ACM SIGPLAN Conference on Programming Language Design and Implementation
• 作者: Samuel Drews;Aws Albarghouthi;Loris D'antoni

Semantic Robustness of Models of Source Code

• DOI: 10.1109/saner53432.2022.00070
• 发表时间: 2020-02
• 期刊: 2022 IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER)
• 作者: Goutham Ramakrishnan;Jordan Henkel;Zi Wang;Aws Albarghouthi;S. Jha;T. Reps

Synthesizing Action Sequences for Modifying Model Decisions

综合修改模型决策的动作序列

• DOI: 10.1609/aaai.v34i04.5996
• 发表时间: 2020
• 期刊: Proceedings of the AAAI Conference on Artificial Intelligence
• 作者: Ramakrishnan, Goutham;Lee, Yun Chan;Albarghouthi, Aws
• 通讯作者: Albarghouthi, Aws

Generating Programmatic Referring Expressions via Program Synthesis

通过程序合成生成程序引用表达式

• 发表时间: 2020
• 期刊: International conference on machine learning
• 作者: Huang, Jiani;Smith, Calvin;Bastani, Osbert;Singh, Rishabh;Albarghouthi, Aws;Naik, Mayur
• 通讯作者: Naik, Mayur

Learning Differentially Private Mechanisms

• DOI: 10.1109/sp40001.2021.00060
• 发表时间: 2021-01
• 期刊: 2021 IEEE Symposium on Security and Privacy (SP)
• 作者: Subhajit Roy;Justin Hsu;Aws Albarghouthi