Collaborative Research: CCRI: ENS: Boa 2.0: Enhancing Infrastructure for Studying Software and its Evolution at a Large Scale

合作研究：CCRI：ENS：Boa 2.0：增强大规模研究软件及其演化的基础设施

• 批准号: 2120448
• 负责人: Hridesh Rajan
• 金额: $ 82.45万
• 依托单位: Iowa State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-10-01 至 2024-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2120448&HistoricalAwards=false
• 关键词: Collaborative; Research; CCRI; ENS; Boa

In today’s software-centric world, ultra-large-scale software repositories, e.g. GitHub, with hundreds of thousands of projects each, are the new library of Alexandria. They contain an enormous corpus of software and information about software. Scientists and engineers alike are interested in analyzing this wealth of information both for curiosity as well as for testing important research hypotheses. However, the current barrier to entry is prohibitive and only a few with well-established infrastructure and deep expertise can attempt such ultra-large-scale analysis. Necessary expertise includes: programmatically accessing version control systems, data storage and retrieval, data mining, and parallelization. The need to have expertise in these four different areas significantly increases the cost of scientific research that attempts to answer research questions involving ultra-large-scale software repositories. As a result, experiments are often not replicable, and reusability of experimental infrastructure low. Furthermore, data associated and produced by such experiments is often lost and becomes inaccessible and obsolete, because there is no systematic curation. Last but not least, building analysis infrastructure to process ultra-large-scale data efficiently can be very hard. This project will continue to enhance the CISE research infrastructure called Boa to aid and assist with such research. This next version of Boa will be called Boa 2.0 and it will continue to be globally disseminated. The project will further develop the programming language also called Boa, that can hide the details of programmatically accessing version control systems, data storage and retrieval, data mining, and parallelization from the scientists and engineers and allow them to focus on the program logic. The project will also enhance the data mining infrastructure for Boa, and a BIGDATA repository containing millions of open source project for analyzing ultra-large-scale software repositories to help with such experiments. The project will integrate Boa 2.0 with the Center for Open Science Open Science Framework (OSF) to improve reproducibility and with the national computing resource XSEDE to improve scalability. The broader impacts of Boa 2.0 stem from its potential to enable developers, designers and researchers to build intuitive, multi-modal, user-centric, scientific applications that can aid and enable scientific research on individual, social, legal, policy, and technical aspects of open source software development. This advance will primarily be achieved by significantly lowering the barrier to entry and thus enabling a larger and more ambitious line of data-intensive scientific discovery in this area.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.

在当今以软件为中心的世界中，超大规模的软件存储库，例如Github每个项目都有数十万个项目，是亚历山大的新图书馆。它们包含大量软件和有关软件的信息。科学家和工程师都有兴趣分析好奇心以及检验重要研究假设的大量信息。但是，当前的进入障碍是禁止的，只有少数具有完善的基础设施和深厚的专业知识可以尝试这种超大规模的分析。必要的专业知识包括：编程访问版本控制系统，数据存储和检索，数据挖掘和并行化。在这四个不同领域拥有专业知识的需求大大增加了科学研究的成本，该科学研究试图回答涉及超大规模软件存储库的研究问题。结果，实验通常是不可复制的，并且实验基础设施的可重复性低。此外，由于没有系统的策展，因此与此类实验相关的数据通常会丢失，并且变得无法访问和过时。最后但并非最不重要的一点是，建立分析基础架构有效地处理超大规模数据可能非常困难。该项目将继续增强称为BOA的CISE研究基础设施，以帮助和协助进行此类研究。下一个版本的BOA将被称为BOA 2.0，它将继续在全球范围内传播。该项目将进一步开发编程语言也称为BOA，该语言可以隐藏科学家和工程师的编程访问版本控制系统，数据存储和检索，数据挖掘以及并行化的详细信息，并允许他们专注于程序逻辑。该项目还将增强BOA的数据挖掘基础架构，并且包含数百万个项目的BigData存储库将BOA 2.0与开放科学开放科学框架（OSF）中心集成在一起，以提高可重复性，并与国家计算资源XSSEDE XSEDE，以提高可伸缩性。 BOA 2.0的更广泛的影响源于其潜力使开发人员，设计师和研究人员能够建立直观，多模式，以用户为中心的科学应用，这些应用可以帮助和启用开源软件开发的个人，社会，法律，政策和技术方面的科学研究。这一进步将主要通过显着降低进入障碍，从而实现该领域的数据密集型科学发现的更大，更雄心勃勃的科学发现线。该奖项反映了NSF的法定任务，并被认为是通过基金会的知识分子优点和更广泛的影响审查标准来通过评估来评估的。

项目成果

Mutation-based Fault Localization of Deep Neural Networks

• DOI: 10.1109/ase56229.2023.00171
• 发表时间: 2023-09
• 期刊: 2023 38th IEEE/ACM International Conference on Automated Software Engineering (ASE)
• 作者: Ali Ghanbari;Deepak-George Thomas;Muhammad Arbab Arshad;Hridesh Rajan

What kinds of contracts do ML APIs need?

• DOI: 10.1007/s10664-023-10320-z
• 发表时间: 2023-07
• 期刊: Empirical Software Engineering
• 影响因子: 4.1
• 作者: S. K. Samantha;Shibbir Ahmed;S. Imtiaz;Hridesh Rajan;G. Leavens

Fix Fairness, Don’t Ruin Accuracy: Performance Aware Fairness Repair using AutoML

• DOI: 10.1145/3611643.3616257
• 发表时间: 2023-06
• 期刊: Proceedings of the 31st ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering
• 作者: Giang Nguyen-;Sumon Biswas;Hridesh Rajan

Fairify: Fairness Verification of Neural Networks

• DOI: 10.1109/icse48619.2023.00134
• 发表时间: 2022-12
• 期刊: 2023 IEEE/ACM 45th International Conference on Software Engineering (ICSE)
• 作者: Sumon Biswas;Hridesh Rajan

Decomposing a Recurrent Neural Network into Modules for Enabling Reusability and Replacement

• DOI: 10.1109/icse48619.2023.00093
• 发表时间: 2022-12
• 期刊: 2023 IEEE/ACM 45th International Conference on Software Engineering (ICSE)
• 作者: S. Imtiaz;Fraol Batole;Astha Singh;Rangeet Pan;Breno Dantas Cruz;Hridesh Rajan