CCRI: ENS: BugSwarm: Enhancing an Infrastructure and Dataset to Support the Software Engineering Research Community

CCRI：ENS：BugSwarm：增强基础设施和数据集以支持软件工程研究社区

• 批准号: 2016735
• 负责人: Cindy Rubio Gonzalez
• 金额: $ 145.44万
• 依托单位: University of California-Davis
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-10-01 至 2025-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2016735&HistoricalAwards=false
• 关键词: CCRI; ENS; BugSwarm; Enhancing; Infrastructure

As technology advances, software plays an ever-increasing role in our daily lives. The prominence of defects is a troublesome fact of software development. Software defects can have serious consequences leading to significant loss of financial capital for businesses or even loss of life. These facts have led to rich areas of research studying previously found defects and their fixes in order to prevent or find future defects. These areas of research span many disciplines from security to software engineering and sub-fields such as program analysis, verification, software testing, and automated program repair. Important to these fields is the availability of a large number of real-world defects for study and evaluation. Recently, the BugSwarm infrastructure was developed to automatically create a continuously growing dataset of reproducible real-world failures and fixes. BugSwarm mines pairs of failures and fixes from open-source GitHub projects that use the continuous integration service Travis CI. The novelty of BugSwarm lies in the automated generation of scripts to compile and test the code, and the use of Docker images to provide the required environment to reproduce each failure and its corresponding fix. The BugSwarm infrastructure has led to the creation of the BugSwarm dataset, which currently includes over 3,000 reproducible failures and fixes from projects written in Java or Python.This research will enhance the BugSwarm infrastructure and dataset to enable growth at the scale and direction required by the research community, and to possess the long-term sustainability required for such an infrastructure to continue evolving. Specifically, the research aims to solve five challenges necessary to make the dataset larger, more robust, diverse, and useful: (1) devise novel techniques to increase the reproduction rate of failures and fixes mined from open-source projects, (2) incorporate user feedback to guide the mining of failures and fixes of interest to the research community, (3) provide a richer classification schema for the dataset, (4) build support for additional programming languages such as JavaScript, and (5) provide a tool ecosystem to facilitate the use of the BugSwarm dataset. As part of this effort, workshops and tutorials will be organized to gather feedback from the community, and to share the BugSwarm resources. Going forward, BugSwarm will not only facilitate experimentation, but also avoid the duplication of work among software engineering researchers.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.

随着技术的进步，软件在我们的日常生活中发挥着越来越重要的作用。缺陷的突出是软件开发中一个令人烦恼的事实。软件缺陷可能会产生严重后果，导致企业遭受重大财务资本损失，甚至造成人员伤亡。这些事实催生了丰富的研究领域，研究以前发现的缺陷及其修复方法，以预防或发现未来的缺陷。这些研究领域跨越从安全到软件工程的许多学科以及程序分析、验证、软件测试和自动化程序修复等子领域。对于这些领域来说，重要的是可以提供大量现实世界的缺陷来进行研究和评估。最近，BugSwarm 基础设施被开发出来，可以自动创建一个不断增长的可重现的现实故障和修复数据集。 BugSwarm 从使用持续集成服务 Travis CI 的开源 GitHub 项目中挖掘成对的故障和修复。 BugSwarm 的新颖之处在于自动生成脚本来编译和测试代码，并使用 Docker 镜像来提供重现每个故障及其相应修复所需的环境。 BugSwarm 基础设施导致了 BugSwarm 数据集的创建，该数据集目前包括来自用 Java 或 Python 编写的项目的 3,000 多个可重现的故障和修复。这项研究将增强 BugSwarm 基础设施和数据集，以实现按照所需的规模和方向进行增长。研究界，并拥有这种基础设施继续发展所需的长期可持续性。具体来说，该研究旨在解决使数据集更大、更稳健、更多样化和更有用所必需的五个挑战：(1) 设计新技术来提高从开源项目中挖掘的故障和修复的重现率，(2) 结合用户反馈来指导研究社区感兴趣的故障和修复的挖掘，(3) 为数据集提供更丰富的分类模式，(4) 构建对 JavaScript 等其他编程语言的支持，以及 (5) 提供工具生态系统方便使用 BugSwarm 数据集。作为这项工作的一部分，我们将组织研讨会和教程来收集社区的反馈，并共享 BugSwarm 资源。展望未来，BugSwarm 不仅会促进实验，还会避免软件工程研究人员之间的重复工作。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力优势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

Fixing dependency errors for Python build reproducibility

• DOI: 10.1145/3460319.3464797
• 发表时间: 2021-07
• 期刊: Proceedings of the 30th ACM SIGSOFT International Symposium on Software Testing and Analysis
• 作者: Suchita Mukherjee;Abigail Almanza;Cindy Rubio-González

On the Reproducibility of Software Defect Datasets

• DOI: 10.1109/icse48619.2023.00195
• 发表时间: 2023-05
• 期刊: 2023 IEEE/ACM 45th International Conference on Software Engineering (ICSE)
• 作者: Hao-Nan Zhu;Cindy Rubio-González

On the Real-World Effectiveness of Static Bug Detectors at Finding Null Pointer Exceptions

• DOI: 10.1109/ase51524.2021.9678535
• 发表时间: 2021-11
• 期刊: 2021 36th IEEE/ACM International Conference on Automated Software Engineering (ASE)
• 作者: David A. Tomassi;Cindy Rubio-González

ActionsRemaker: Reproducing GITHUB Actions

ActionsRemaker：复制 GITHUB Actions

• DOI: 10.1109/icse-companion58688.2023.00015
• 发表时间: 2023
• 期刊: IEEE
• 作者: Zhu, Hao-Nan;Guan, Kevin Z.;Furth, Robert M.;Rubio-González, Cindy
• 通讯作者: Rubio-González, Cindy