SHF: Small: Deep Learning Software Repositories

SHF：小型：深度学习软件存储库

• 批准号: 1525902
• 负责人: Denys Poshyvanyk
• 金额: $ 40万
• 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: 2015
• 资助国家: 美国
• 起止时间: 2015-09-01 至 2019-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1525902&HistoricalAwards=false
• 关键词: SHF; Small; Deep; Learning; Software

Improvements in both computational power and the amount of memory in modern computer architectures, have enabled new approaches to canonical machine learning tasks. Specifically, these architectural advances have enabled machines, which are capable of learning deep compositional representations of massive data repositories. The rise of deep learning has ushered tremendous advances in several fields, and, given the complexity of software repositories, our hypothesis is that deep learning has the potential to usher new analytical frameworks and methodologies for Software Engineering research as well practice.The research program addresses three main goals by applying deep learning where conventional machine learning has been used before. First is the design of new models based on deep architectures for Software Engineering tasks. The project will develop deep software language models for sequence analysis tasks and deep information retrieval models for document analysis tasks. Second, the project will apply the internal representations to practical problems in Software Engineering by instantiating deep learning to support tasks such as code suggestion, improving software lexicons, model-based testing, code search and clone detection. Third, the project will conduct empirical evaluations designed to demonstrate ways of modeling software artifacts that will inform entirely novel suites of learned features that can be used from task to task. The move from traditional machine learning to deep learning will improve results in many software analysis tasks and in empirical Software Engineering research.

现代计算机体系结构中计算能力和内存量的改进，已经为规范机器学习任务提供了新的方法。具体来说，这些架构的进步使机器能够学习大量数据存储库的深度组合表示。深度学习的兴起在多个领域带来了巨大的进步，鉴于软件库的复杂性，我们的假设是，深度学习有可能为软件工程研究和实践带来新的分析框架和方法。该研究计划通过将深度学习应用于传统机器学习之前，实现了三个主要目标。首先是基于软件工程任务的深层架构设计新模型。该项目将开发用于序列分析任务的深度软件语言模型和用于文档分析任务的深度信息检索模型。其次，该项目将通过实例化深度学习将内部表示应用于软件工程中的实际问题，以支持代码建议，改进软件词典，基于模型的测试，代码搜索和克隆检测等任务。第三，该项目将进行经验评估，旨在展示建模软件工件的方法，这些软件工件将告知可以从任务到任务使用的全新的学习功能套件。从传统机器学习到深度学习的转变将改善许多软件分析任务和经验软件工程研究的结果。