SHF: Small: Deep Learning Software Repositories

在现代计算机体系结构中，计算能力和存储量的改进使得能够采用新的方法来执行规范的机器学习任务。具体地说，这些体系结构的进步使机器能够学习海量数据存储库的深层组成表示。深度学习的兴起在几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。该研究计划通过在以前使用传统机器学习的地方应用深度学习来解决三个主要目标。首先是基于软件工程任务的深层架构的新模型的设计。该项目将为序列分析任务开发深层软件语言模型，为文件分析任务开发深层信息检索模型。其次，该项目将通过实例化深度学习来将内部表示应用于软件工程中的实际问题，以支持诸如代码建议、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，该项目将进行经验性评估，旨在演示建模软件构件的方法，这些方法将向全新的套件提供可在任务之间使用的学习功能。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。

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.