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: 2120386
• 负责人: Tien Nguyen
• 金额: $ 44.82万
• 依托单位: University of Texas at Dallas
• 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=2120386&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）是亚历山大的新图书馆，它们包含大量的软件和有关软件的信息。有兴趣分析这些丰富的信息，既是出于好奇，也是为了测试重要的研究假设。然而，目前的进入壁垒令人望而却步，只有少数拥有完善基础设施和深厚专业知识的人才能尝试如此大规模的研究。必要的专业知识包括：以编程方式访问版本控制系统、数据存储和检索、数据挖掘以及并行化。对这四个不同领域的专业知识的需求显着增加了试图回答涉及超大规模的研究问题的科学研究的成本。因此，实验通常不可复制，实验基础设施的可重用性也很低，而且，由于没有系统性的管理，此类实验产生的数据常常会丢失、无法访问和过时。但并非最不重要的是，构建有效处理超大规模数据的分析基础设施可能非常困难，该项目将继续增强名为 Boa 的 CISE 研究基础设施，以帮助和协助此类研究。Boa 的下一个版本将称为 Boa。 2.0 并将继续在全球范围内进一步传播，该项目将开发也称为 Boa 的编程语言，该语言可以向科学家和工程师隐藏以编程方式访问版本控制系统、数据存储和检索、数据挖掘以及并行化的细节。该项目还将增强 Boa 的数据挖掘基础设施，以及包含数百万个开源项目的 BIGDATA 存储库，用于分析超大规模软件存储库，以帮助进行此类实验。 Boa 2.0 与开放科学中心开放科学框架 (OSF) 一起提高可重复性，并与国家计算资源 XSEDE 一起提高可扩展性 Boa 2.0 的更广泛影响源于其为开发人员、设计人员提供支持的潜力。和研究人员构建直观、多模式、以用户为中心的科学应用程序，这些应用程序可以帮助和支持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。这一进步主要是通过显着降低成本来实现的。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

DeepVD: Toward Class-Separation Features for Neural Network Vulnerability Detection

DeepVD：面向神经网络漏洞检测的类分离特征

• DOI: 10.1109/icse48619.2023.00189
• 发表时间: 2023-05-01
• 期刊: 2023 IEEE/ACM 45th International Conference on Software Engineering (ICSE)
• 作者: Wenbo Wang;Tien N. Nguyen;Shaohua Wang;Yi Li;Jiyuan Zhang;Aashish Yadavally
• 通讯作者: Aashish Yadavally

(Partial) Program Dependence Learning

（部分）程序依赖学习

• DOI: 10.1109/icse48619.2023.00209
• 发表时间: 2023-05-01
• 期刊: 2023 IEEE/ACM 45th International Conference on Software Engineering (ICSE)
• 作者: Aashish Yadavally;T. Nguyen;Wenbo Wang;Shaohua Wang
• 通讯作者: Shaohua Wang