Workshop on Deep Learning and Software Engineering

深度学习与软件工程研讨会

• 批准号: 1945999
• 负责人: Baishakhi Ray
• 金额: $ 4.99万
• 依托单位: Columbia University
• 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: 2019-09-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1945999&HistoricalAwards=false
• 关键词: Workshop; Deep; Learning; Software; Engineering

This award supports a workshop to explore synergies between Deep Learning and Software Engineering. The goal is to accelerate research that uses Deep Learning in research and practice to improve techniques and tools for Software Engineering through the power of Deep Learning. Conversely, deep-learning based systems, which are emerging in many application domains, need new Software Engineering approaches render them correct, reliable and comprehensible. The workshop will bring together researchers and practitioners in both fields to discuss research priorities community resources needed to accelerate research in the intersection of Deep Learning and Software Engineering. Deep Learning represents a fundamental shift in the manner by which machines learn patterns from data by automatically extracting salient features for a given computational task, as opposed to relying upon human intuition. Deep Learning approaches are characterized by architectures comprised of several layers that perform mathematical transformations, according to sets of learnable parameters, on data passing through them. These computational layers and parameters form models that can be trained for specific tasks, such as image classification, by updating the parameters according to a model?s performance on a labeled set of training data. Given the immense amount of data in software repositories that can serve as training data, deep learning techniques have ushered in advancements across a range of tasks in software engineering research including automatic software repair, code suggestion, defect prediction, malware detection, feature location, and many others. The workshop will review the state of the research and practice and give guidance to the community about opportunities and challenges.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.

该奖项支持举办一个探讨深度学习和软件工程之间协同作用的研讨会。目标是加速在研究和实践中使用深度学习的研究，通过深度学习的力量改进软件工程的技术和工具。相反，在许多应用领域中出现的基于深度学习的系统需要新的软件工程方法来使其正确、可靠和易于理解。该研讨会将汇集两个领域的研究人员和从业者，讨论加速深度学习和软件工程交叉领域研究所需的研究重点社区资源。深度学习代表了机器通过自动提取给定计算任务的显着特征来从数据中学习模式的方式的根本转变，而不是依赖人类直觉。深度学习方法的特点是由多个层组成的架构，这些层根据可学习参数集对通过它们的数据执行数学转换。 这些计算层和参数形成可以针对特定任务（例如图像分类）进行训练的模型，方法是根据模型在标记的训练数据集上的性能更新参数。鉴于软件存储库中存在大量可用作训练数据的数据，深度学习技术在软件工程研究的一系列任务中取得了进步，包括自动软件修复、代码建议、缺陷预测、恶意软件检测、特征定位和还有很多其他的。该研讨会将回顾研究和实践的现状，并就机遇和挑战向社区提供指导。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。