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.

该奖项支持一个研讨会，以探索深度学习和软件工程之间的协同作用。目的是加速研究和实践中使用深度学习的研究，以通过深度学习的力量来改善软件工程的技术和工具。相反，基于深度学习的系统在许多应用程序域中出现，需要新的软件工程方法使它们正确，可靠且可理解。该研讨会将在两个领域的研究人员和从业人员聚集在一起，讨论研究优先事项社区资源，以加速深度学习与软件工程的交集所需的研究。深度学习代表了机器通过自动为给定计算任务提取显着特征而不是依靠人类直觉的方式从数据中学习模式的方式的根本转变。深度学习方法的特征是架构由几个层组成的层次组成，这些层是根据可学习的参数进行数学转换的，这些层次是通过它们通过它们的数据。 这些计算层和参数形成模型，可以通过根据模型的性能在标记的一组训练数据集上更新参数来培训特定任务，例如图像分类。鉴于可以用作培训数据的软件存储库中的大量数据，深度学习技术已经迎来了软件工程研究中一系列任务的进步，包括自动软件维修，代码建议，缺陷预测，恶意软件检测，功能位置，功能位置和许多其他。该研讨会将审查研究和实践的状况，并向社区提供有关机遇和挑战的指导。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛的影响评估标准通过评估来支持的。