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.

人工智能的力量，更具体地说，是机器学习(ML)，正在使新类别的软件应用成为可能。软件工程(SE)的目标，如构建正确的软件，以及为测试和调试设计有效的软件开发过程，当ML嵌入到系统中时，需要新的技术，以便测试学习模型、验证训练数据和测试有效结果。研究人员已经开始致力于提高基于ML学习的应用软件的质量和生产率。相反，SE研究人员正在使用ML来加速SE研究的进展，以获得更好的结果。这笔急切的拨款是为了在ML和SE的交叉点上构建一个研究路线图，并为那些在这些领域进行研究的人提供建议。该项目将主要集中在一种名为深度学习(深度学习)的特定学习模式上。议程是双向的：使用数字图书馆来加速SE的研究，使用数字图书馆来解决基于数字图书馆的应用程序的正确性和质量问题。该项目将调查DL已经取得或承诺取得重大进展的SE任务的类型，并将确定SE研究和改进基于DL的应用程序的领域。该项目将提供这一研究领域在数据收集、表示和处理方面的最新知识和最佳做法，并探索共同的基础设施、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交叉点未来研究和教育目的的活的/不断增长的工作储存库。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。