EAGER: Mapping Future Synergies between Deep Learning and Software Engineering

EAGER：绘制深度学习与软件工程之间的未来协同效应

• 批准号: 1927679
• 负责人: Denys Poshyvanyk
• 金额: $ 8.28万
• 依托单位: College of William and Mary
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-07-15 至 2020-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1927679&HistoricalAwards=false
• 关键词: EAGER; Mapping; Future; Synergies; between

The power of AI, more specifically, machine learning (ML), is enabling new classes of software applications. Software Engineering (SE) goals such as construction of correct software, and devising effective software development processes for testing and debugging, require new techniques when ML is embedded in a system, in order to test learning models, validate training data, and test for valid results. Researchers have begun working toward improving the quality and productivity of software for ML-learning-based applications. Conversely, SE researchers are using ML to accelerate advances in the SE research in order to achieve better results. This EAGER grant is for the purpose of assembling a research road map in the intersection of ML and SE, and to make recommendations for those doing research in these areas.The project will focus mainly on a specific mode of learning called Deep Learning (DL). The agenda is bidirectional: use of DL to accelerate research in SE, and SE to address correctness and quality of DL-based applications. The project will survey types of SE tasks where DL has achieved or promised significant advances, and it will identify areas where DL-based applications have been studied and improved by SE. The project will make available the current state of knowledge and best practices for data gathering, representation and processing for this research area and explore common infrastructure, architectures, data resources and benchmarks. The project will construct a systematic literature review that will become a living/growing repository of work in the SE/DL intersection for future research and education purposes.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.

人工智能（更具体地说，机器学习 (ML)）的力量正在催生新型软件应用程序。 当 ML 嵌入到系统中时，软件工程 (SE) 的目标（例如构建正确的软件以及设计用于测试和调试的有效软件开发流程）需要新技术，以便测试学习模型、验证训练数据并测试有效性结果。研究人员已开始致力于提高基于机器学习学习的应用程序的软件质量和生产力。相反，SE 研究人员正在使用 ML 来加速 SE 研究的进展，以取得更好的结果。 这项 EAGER 资助的目的是构建 ML 和 SE 交叉领域的研究路线图，并为这些领域的研究人员提出建议。该项目将主要关注一种称为深度学习 (DL) 的特定学习模式。议程是双向的：使用 DL 加速 SE 的研究，并使用 SE 来解决基于 DL 的应用程序的正确性和质量。该项目将调查 DL 已取得或有望取得重大进展的 SE 任务类型，并将确定 SE 研究和改进的基于 DL 的应用程序的领域。该项目将提供该研究领域数据收集、表示和处理的当前知识状态和最佳实践，并探索通用基础设施、架构、数据资源和基准。该项目将构建一个系统的文献综述，它将成为 SE/DL 交叉点的一个活生生的/不断增长的工作存储库，用于未来的研究和教育目的。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力评估进行评估，被认为值得支持。优点和更广泛的影响审查标准。