一句话：提出 FRMiner，用深度孪生网络与少样本学习从聊天消息中检测隐藏的功能请求对话。
在线聊天在软件开发中日益普及，开发者在讨论功能时可能透露出期望的功能，因此自动从海量聊天消息中挖掘功能请求有助于需求获取。然而，检测对话中的功能请求需要充分理解上下文信息，且为学习而标注功能请求对话的代价极高。为弥合这一差距，作者将传统的“单对话映射到类别”的文本分类任务重构为“判断两个对话是否相似”的少样本学习任务，并提出 FRMiner 方法，借助深度孪生网络检测聊天消息中的功能请求对话；其中设计了基于 BiLSTM 的对话模型，可从前向和反向两个方向学习对话的上下文信息。在真实世界项目上的评估显示，该方法的平均精确率、召回率和 F1 值分别为 88.52%、88.50% 和 88.51%，证实其能够有效检测聊天消息中隐藏的功能请求，从而以自动化方式促进从群体中收集全面的需求。
Online chatting is gaining popularity and plays an increasingly significant role in software development. When discussing functionalities, developers might reveal their desired features to other developers. Automated mining techniques towards retrieving feature requests from massive chat messages can benefit the requirements gathering process. But it is quite challenging to perform such techniques because detecting feature requests from dialogues requires a thorough understanding of the contextual information, and it is also extremely expensive on annotating feature-request dialogues for learning. To bridge that gap, we recast the traditional text classification task of mapping single dialog to its class into the task of determining whether two dialogues are similar or not by incorporating few-shot learning. We propose a novel approach, named FRMiner, which can detect feature-request dialogues from chat messages via deep Siamese network. We design a BiLSTM-based dialog model that can learn the contextual information of a dialog in both forward and reverse directions. Evaluation on the real-world projects shows that our approach achieves average precision, recall and F1-score of 88.52%, 88.50% and 88.51%, which confirms that our approach could effectively detect hidden feature requests from chat messages, thus can facilitate gathering comprehensive requirements from the crowd in an automated way.
DOI 原文 ·
@inproceedings{paperbot5605,
title = {Detection of hidden feature requests from massive chat messages via deep siamese network},
author = {Lin Shi and Mingzhe Xing and Mingyang Li and Yawen Wang and Shoubin Li and Qing Wang},
booktitle = {ICSE},
year = {2020},
doi = {10.1145/3377811.3380356}
}