一句话：提出 F2CHAT，通过融合手工非文本特征与深度文本特征自动分类开发者聊天线程的信息类型。
开源软件开发者常通过在线聊天室交流，其中大量讨论线程以问答形式承载丰富信息，但聊天数据短暂、非结构化且对话相互纠缠，线程级信息检索困难。本文先通过卡片分类人工考察 Gitter 三个聊天室的数据，建立包含九类信息的线程级分类法，并标注 2,959 个线程；进而提出 F2CHAT 分类方法，融合手工非文本特征与神经模型提取的深度文本特征，分为两阶段：第一阶段用孪生架构预训练文本特征编码器，第二阶段实现两类特征的深度融合。评估显示 F2CHAT 平均 F1 为 0.628，较基线提升 57%，并在项目内和跨项目验证中证明了非文本特征的有效性。
Online chatrooms are gaining popularity as a communication channel between widely distributed developers of Open Source Software (OSS) projects. Most discussion threads in chatrooms follow a Q&A format, with some developers (askers) raising an initial question and others (respondents) joining in to provide answers. These discussion threads are embedded with rich information that can satisfy the diverse needs of various OSS stakeholders. However, retrieving information from threads is challenging as it requires a thread-level analysis to understand the context. Moreover, the chat data is transient and unstructured, consisting of entangled informal conversations. In this paper, we address this challenge by identifying the information types available in developer chats and further introducing an automated mining technique. Through manual examination of chat data from three chatrooms on Gitter, using card sorting, we build a thread-level taxonomy with nine information categories and create a labeled dataset with 2,959 threads. We propose a classification approach (named F2CHAT) to structure the vast amount of threads based on the information type automatically, helping stakeholders quickly acquire their desired information. F2CHAT effectively combines handcrafted non-textual features with deep textual features extracted by neural models. Specifically, it has two stages with the first one leveraging the siamese architecture to pretrain the textual feature encoder, and the second one facilitating an in-depth fusion of two types of features. Evaluation results suggest that our approach achieves an average F1-score of 0.628, which improves the baseline by 57%. Experiments also verify the effectiveness of our identified non-textual features under both intra-project and cross-project validations.
DOI 原文 ·
@inproceedings{paperbot6516,
title = {Automating Developer Chat Mining},
author = {Shengyi Pan and Lingfeng Bao and Xiaoxue Ren and Xin Xia and David Lo and Shanping Li},
booktitle = {ASE},
year = {2021},
doi = {10.1109/ase51524.2021.9678923}
}