一句话：通过Wizard of Oz实验标注bug修复对话并训练监督学习模型自动检测言语行为类型，以推进软件工程虚拟助手。
本文针对 bug 修复对话中的言语行为检测问题，旨在推动软件工程虚拟助手的发展。作者开展了一项“Wizard of Oz”实验，30 名专业程序员在约两小时内修复 bug，并通过模拟虚拟助手寻求帮助。随后采用开放式编码进行人工标注，识别对话中的言语行为类型，并训练和评估有监督学习算法以自动检测这些类型。在 30 段两小时对话中，共产生 2459 条标注，发现 26 种言语行为类型；自动检测取得 69% 的精度和 50% 的召回率。该工作强调相关数据与实验的缺乏是软件工程虚拟助手落后于其他领域应用的重要原因。
This paper targets the problem of speech act detection in conversations about bug repair. We conduct a ``Wizard of Oz'' experiment with 30 professional programmers, in which the programmers fix bugs for two hours, and use a simulated virtual assistant for help. Then, we use an open coding manual annotation procedure to identify the speech act types in the conversations. Finally, we train and evaluate a supervised learning algorithm to automatically detect the speech act types in the conversations. In 30 two-hour conversations, we made 2459 annotations and uncovered 26 speech act types. Our automated detection achieved 69% precision and 50% recall. The key application of this work is to advance the state of the art for virtual assistants in software engineering. Virtual assistant technology is growing rapidly, though applications in software engineering are behind those in other areas, largely due to a lack of relevant data and experiments. This paper targets this problem in the area of developer Q/A conversations about bug repair.
@inproceedings{paperbot4598,
title = {Detecting speech act types in developer question/answer conversations during bug repair},
author = {Andrew Wood and Paige Rodeghero and Ameer Armaly and Collin McMillan},
booktitle = {FSE},
year = {2018},
doi = {10.1145/3236024.3236031}
}