CI-EN: Boa: Enhancing Infrastructure for Studying Software and its Evolution at a Large Scale

CI-EN：Boa：增强大规模研究软件及其演化的基础设施

• 批准号: 1513263
• 负责人: Hridesh Rajan
• 金额: $ 142.69万
• 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: 2015
• 资助国家: 美国
• 起止时间: 2015-06-01 至 2021-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1513263&HistoricalAwards=false
• 关键词: CI; EN; Boa; Enhancing; Infrastructure

In today's software-centric world, ultra-large-scale software repositories, e.g., SourceForge, GitHub, and Google Code, with hundreds of thousands of projects each, are the new library of Alexandria. They contain an enormous corpus of software and information about software and software projects. Scientists and engineers alike are interested in analyzing this wealth of information to test important research hypotheses. However, the current barrier to entry is prohibitive because deep expertise and sophisticated tools are needed to write programs that access version control systems, store and retrieve workable data subsets, and perform the needed ultra-large-scale analysis. The goal is accelerate the pace of Software Engineering research and to increase reusability and replicability, while properly curating the data and analyses. This project is building a CISE research infrastructure called Boa to aid and assist with such research and will be globally available. The project designs a new programming language 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 also designs a data mining infrastructure for Boa, and a BIGDATA repository containing 700,000+ open source projects for analyzing ultra-large-scale software repositories to help with such experiments. The broader impacts of Boa 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.

在当今以软件为中心的世界中，超大规模的软件库，如SourceForge、GitHub和Google Code，每个都有数十万个项目，是Alexandria的新库。它们包含一个庞大的软件语料库以及关于软件和软件项目的信息。科学家和工程师都对分析这些丰富的信息以检验重要的研究假设感兴趣。然而，目前的进入障碍是令人望而却步的，因为需要深厚的专业知识和复杂的工具来编写访问版本控制系统、存储和检索可用数据子集以及执行所需的超大规模分析的程序。目标是加快软件工程研究的步伐，增加可重用性和可复制性，同时适当地管理数据和分析。该项目正在建设一个名为BOA的CEISE研究基础设施，以帮助和协助此类研究，并将在全球范围内提供。该项目设计了一种新的编程语言，可以对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、数据挖掘和并行化的细节，并允许他们专注于程序逻辑。该项目还为Boa设计了一个数据挖掘基础设施，以及一个包含70多万个开源项目的BigData存储库，用于分析超大规模的软件存储库，以帮助进行此类实验。Boa的更广泛影响源于它的潜力，它使开发人员、设计人员和研究人员能够构建直观的、多模式的、以用户为中心的科学应用程序，这些应用程序可以帮助并支持对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。这一进展将主要通过大幅降低准入门槛来实现，从而在这一领域实现更大范围和更雄心勃勃的数据密集型科学发现。

项目成果

On decomposing a deep neural network into modules

• DOI: 10.1145/3368089.3409668
• 发表时间: 2020-11
• 期刊: Proceedings of the 28th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering
• 作者: Rangeet Pan;Hridesh Rajan

Repairing Deep Neural Networks: Fix Patterns and Challenges

• DOI: 10.1145/3377811.3380378
• 发表时间: 2020-05
• 期刊: 2020 IEEE/ACM 42nd International Conference on Software Engineering (ICSE)
• 作者: Md Johirul Islam;Rangeet Pan;Giang Nguyen;Hridesh Rajan