SHF: Small: Enabling Software Engineering Virtual Assistant Technology

本研究项目的目标是解决在为软件工程创建虚拟助手时采用自然语言处理技术的关键障碍。Siri、Cortana和Alexa等虚拟助手在日常任务的计算中扮演着越来越重要的角色，但现有的虚拟助手技术在应用于软件工程任务方面存在诸多障碍。该项目的长期目标是，虚拟助手将提高软件工程师的工作效率。该建议针对其中的两个障碍：1)会话分析和建模，以及2)引用表达式生成。简而言之，第一个问题是自然语言建模对话中的实验倾向于涵盖具有相似结果的主题，而关于软件的对话可能具有更广泛的可能结果。第二个问题是，自然语言处理的许多研究都集中在人类如何引用具有普遍首选属性的物理对象，而相反，软件工件往往不具有人们用作描述的可测量属性。主要的更广泛的影响是对视障人士的辅助技术的应用。虚拟助手通过创建语音界面来回答软件开发问题，有可能减轻视障人士进入计算机行业的障碍。

The objective of this research project is to address key barriers in adapting natural language processing techniques to problems in creating virtual assistants for software engineering. Virtual assistants such as Siri, Cortana, and Alexa are claiming an increasing role in computing for everyday tasks, but multiple barriers prevent existing virtual assistant technology from being applied to software engineering tasks. The long-term goal of the project is that virtual assistants will improve productivity for software engineers.This proposal targets two of those barriers: 1) conversation analysis and modeling, and 2) reference expression generation. The first of these problems, in a nutshell, is that experiments in natural language modeling conversations tend to cover topics with similar outcomes, while conversations about software may have a much wider range of possible outcomes. The second problem is that much research in natural language processing is focused on how humans refer to physical objects that have attributes that are universally preferred while in contrast, software artifacts tend not to have measurable attributes that people use as descriptions. The chief broader impact is an application to assistive technology for persons who are visually impaired. Virtual assistants have the potential to alleviate barriers-to-entry into computing careers faced by visually impaired persons by creating a voice interface for answering software development questions.